CN104865951A - Cigarette tobacco cutting process tobacco flake preprocessing stage on-line monitoring and fault diagnosis method - Google Patents

Abstract

The invention discloses a cigarette tobacco cutting process tobacco flake preprocessing stage on-line monitoring and fault diagnosis method. As for key equipment in a tobacco flake preprocessing stage such as TB-L loosing and conditioning equipment and KAS primary feeding equipment, the problem of incapability of realizing accurate monitoring and fault diagnosis which is caused by a large number of batches, slow time varying, uncertain operation time and diversity of products may be brought about, while, with the method of the invention adopted, the problem can be solved. The method includes the following steps that: operating data of batch, time and attribute three-dimensional characteristics are unfolded according to attribute directions through process characteristic analysis, so that the problem of unequalness of the length of data of different batches can be solved; a monitoring model of each kind of flake product trademark is established respectively through adopting a principal component analysis (PCA) and based on a multi-model structure, and the T<2> and SPE statistics and control limits thereof of monitoring models of different trademarks are calculated in an off-line manner; process operating data of a tobacco flake preprocessing stage are acquired in an online manner, and corresponding monitoring models are called according to product trademarks, so that the T<2> and SPE statistics can be calculated in an online manner; and when any index exceeds the control limit of a normal operating area, a contribution chart method is adopted to perform fault diagnosis.

Description

A kind of cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method

Technical field

The present invention relates to cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing technology, particularly relate to the on-line monitoring and fault diagonosing method of loosening steam conditioner and a charger.

Background technology

Tobacco is Important Economic strength and the revenue streams of country, and China's tobacco tax revenue accounts for 8% ~ 10% of government finance income.Due to the great change of tobacco business globalization, new laws and regulations and external environment condition, Cigarette Industrial Enterprise faces more and more severe competitive pressure and social pressures, progressively promoting cigarette product quality control and precognition equipment maintenance level, is the effective way and the inexorable trend that realize Cigarette Industrial Enterprise " equipment state controlled and Effec-tive Function " target.

Production of cigarettes process belongs to typical flow manufacturing batch process, and the product grade predetermined according to the production schedule and batch number carry out the production of pipe tobacco and cigarette.Production of cigarettes comprises the large main process of throwing and wraparound two, and wherein Primary Processing is according to the characteristic of raw tobacco material, successively through smoked sheet pre-service, cut tobacco processed, mix with addition of technological processes such as perfume (or spice), sheet cigarette is made the process of qualified pipe tobacco.

At present, on-line monitoring and fault diagonosing research for cigarette primary processing process smoked sheet pretreatment section key equipment is main based on continuous process univariate statistics course control method for use, facing production course monitoring demand, utilizes rainbow figure and Measure of Process Capability analyze the smoked sheet pretreatment section process variable being in hot and humid condition and diagnose.Document " Mishra B; Dangayach G S.Performanceimprovement through statistical process control:a longitudinal study [J] .International Journal of Globalisation and Small Business; 2009; 3 (1): 55-72 " describes the application implementation of statistical process control method in cigar mill of Nepal, effectively improves the Measure of Process Capability of equipment.In order to improve throwing quality monitoring precision, Shanghai Cigarette Factory " Zhang Min; Tong Yigang; Dai Zhiyuan; wait the Preliminary Applications of .SPC technology in throwing quality management [J]. tobacco science and technology, 2004, (9): 10-11 " attempt applied statistics process control technology first, establish a set of process manufacturing capability appraising system, solve the problem that real process Capability index is on the low side.To become more meticulous manufacture to promote enterprise, Changsha Cigarette Factory " yellow victory; Li Jianhui; Zhang Yongchuan. Changsha Cigarette Factory SPC systematic difference is put into practice [J]. Chinese tobacco journal; 2008; 14 (S1): 14-17 " propose the principle of cigarette enterprise applied statistics process control, and describe statistical process control system in the applicable cases of Primary Processing and effect.Stablizing in order to ensure Primary Processing technological parameter, Kunming, Shanxi tobacco company limited " Li Wenquan; Zhao Wentian; Li Wenbin. the application [J] of statistical process control technology SPC in tobacco cutting is produced. mechanical engineering and robotization; 2009; (5): 116-118 " applied statistics process control technology establishes throwing Quality Monitoring Control System, ensure that consistance and the stability of tobacco quality.In order to improve Enterprises Quality Management level, Chenzhou cigar mill " Luojiang County; the Liu Qiang application practice of refined .SPC system in Cigarette Industrial Enterprise quality management [J]. industrial economy; 2011; (3): 67-72 " from methods such as application flow, major function, data validitys, detailed design is carried out to statistical process control system, utilize rainbow figure and Measure of Process Capability monitor Primary Processing and analyze.In order to ensure that cigarette homogeneity is produced and continues Improving The Quality of Products, Nanchang Cigarette Factory " Li Tiejun; Yang get Qiang; Li Qiang .SPC system is in the application [J] of cigarette primary processing Process Quality Control. Chinese quality; 2013; (4): 87-88 " construct the statistical process control system integrating data acquisition, process monitoring, process analysis procedure analysis, abnormality processing, quality examination etc., realize the detection and diagnosis of Primary Processing critical process and emphasis parameter.The problem of unified Quality Process horizontal parameters evaluation system is lacked for tobacco productive corporation process, Corpus--based Method process control technology, Qingdao cigar mill " Zhu Min; Wang Peichen; Zhang Xueli; etc. cigarette product Ore-controlling Role [J] the .PLC & FA of Corpus--based Method process control; 2014; (3): 58-62 " propose control from plant site real-time quality, Quality Mgmt Dept's gated data analyzes three layers of production run quality control system of enterprise-quality decision-making, achieves throwing and wraparound whole-process quality management.In order to realize the whole process supervision of production of cigarettes process and review, Hangzhou Cigarette Factory " money is outstanding; Xu Jin; Ji Qi; etc. the application of manufacturing execution system in tobacco enterprise [J]. mechanicnl manufacture and automation, 2014,43 (2): 147-149 " establish manufacturing execution system, by to the effective integration of throwing managing and control system, wraparound digital management system and quality testing analytic system data and analysis, rainbow figure and Measure of Process Capability is adopted to monitor Primary Processing and analyze.In addition, document " Ji Shengqiang; Cheng Jingjing; Li Jun. based on the cigarette primary processing product quality monitoring technique study [J] of SPC and neural network. industrial control computer; 2011; 24 (12): 65-68 " for cigarette primary processing process existing statistical process control system method for supervising Problems existing, the moving window formula control chart being applicable to on-line monitoring is proposed, and two the BP neural network models established respectively for control chart pattern-recognition and mass defect cause diagnosis, effectively improve the recognition efficiency of control chart, add the validity of quality monitoring.

The magnanimity service data that cigarette primary processing process has accumulated not yet makes full use of, ubiquity " enrich by data, poor information " problem, above-mentioned research is more the process characteristic change paid close attention in same batch of a certain leaf product trade mark, only be confined to the research of single time shaft, do not consider that cigarette primary processing production run belongs to the intrinsic propesties manufacturing batch process continuously, lack the effective analysis to multidate information on batch axle, accurately can not disclose the dynamic of various process variable between different leaves product grade and different production batch, cannot complicated incidence relation change between detection and diagnosis process variable, make in current smoked sheet pretreatment section, reliability and the accuracy of univariate statistics course control method for use detection and diagnosis result have much room for improvement.

Summary of the invention

For solving problems of the prior art, the invention provides a kind of cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method, three-dimensional data analytical approach towards batch process is introduced in the on-line monitoring and fault diagonosing of cigarette primary processing process smoked sheet pretreatment section by the method, passes through T ², SPE two multivariate statistics amount on-line monitoring faults, determine by contribution plot method the primary process variable causing fault, solve the smoked sheet pretreatment section detection and diagnosis result reliability that multiple batches of, slow time-varying, running time uncertain and product diversity causes, the problem that accuracy is not high preferably.

Concrete technical scheme of the present invention is as follows:

A kind of cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method, comprising:

1) the different loosening and gaining moisture of production batch of different leaves product grade and the service data of charging (feeding) equipment is obtained, have I production batch, a J measurand and K sampled point for a certain product grade, the data obtained can be expressed as a three-dimensional data matrix;

2) mode that three-dimensional data matrix is launched according to attribute obtains two-dimensional data matrix, carries out average centralization and normalized square mean process to two-dimensional data matrix, obtains the modeling data X (IK × J) of a certain product grade monitoring model;

3) PCA decomposition is carried out to the modeling data X (IK × J) of the different product trade mark, set up the PCA monitoring model of Multi-model MPCA;

4) T of calculated off-line different product trade mark monitoring model ², SPE monitoring and statistics amount, according to the T of the different production batch of the like products trade mark ²value obeys F distribution, SPE value obeys χ ²distribution, determines the T of each monitoring model under confidence degree ²limit is controlled with SPE;

5) during on-line monitoring, the process data x of Real-time Collection loosening and gaining moisture and charging (feeding) equipment _new(1 × J), calls corresponding PCA monitoring model according to current production type, calculates data x _newthe T that (1 × J) is corresponding ²statistic and SPE statistic; Compare the control limit that two statistic indexs are respective with it in real time, if within two statistic indexs are all positioned at and control limit, show process and equipment normal operation, if one of them statistic index exceeds control limit, show that process and equipment have unusual condition;

6) when detecting that process and equipment have unusual condition, calculating the contribution margin of each process variable to the statistic that transfinites, wherein contributing larger variable to be tentatively defined as causing the causal variable of process and unit exception.

In step 1) in, to the smoothing process of process variable of continuous multiple sampled point, form three-dimensional modeling data matrix, described smoothing processing is do arithmetic mean to continuous 6 sampled datas of a certain process variable to obtain valid data, to overcome the impact of process random perturbation.

In the present invention, the attribute of three-dimensional data launches and pre-service, by process operation specificity analysis, only a steady working condition is there is in the same production batch of a certain product grade, namely in same production batch, not there is multiple stable operating point, mutual relationship between explanatory variable has identical process feature, causes the sampled data Length discrepancy between different batches because the running time is uncertain simultaneously.The mode launched according to attribute is adopted to obtain two-dimensional data matrix based on above two aspect analyses, and average centralization and normalized square mean process are carried out to 2-D data, obtain the modeling data of a certain product grade monitoring model, therefore, step 2) in launch after two-dimensional matrix be X (IK _i× J).

Wherein, step 2) in pre-service comprise carry out successively subtract average, except standard deviation process.

In step 3) in, the same row element of pretreated two-dimensional matrix is considered as modeling data collection X=[x ₁, x ₂..., x _ik..., x _iK], carry out PCA decomposition to X, the computing formula that PCA decomposes is as follows:

$X=T_A{P_A}^T+E=\underset{a=1}{\overset{A}{\mathrm{\&Sigma;}}}{t}_a{p}_a^T+E$

Wherein A is pivot number, and a represents that different PCA decomposes direction, T _arepresent that (IK × A) after retaining A pivot ties up score matrix, P _arepresent that (J × A) after retaining A pivot ties up load matrix, E is residual matrix.

Described step 4) in, T ²the computing formula of statistic is:

T ² _ik＝t _ikS ^-1t _ik ^Tik＝1,2,…,IK

Wherein t _ik=x _ikp _arepresent the pivot score vector that (1 × J) ties up, diagonal matrix S=diag (λ ₁..., λ _a) be by the covariance matrix Σ X of modeling data collection X ^tfront A the eigenwert of X formed;

T ²the control limit of statistic utilizes F to distribute and adopts following formula calculating:

$T^2~\frac{A(N-1)}{N-A}{F}_{\mathrm{\&alpha;}}(A,N-A)$

Wherein A is the pivot number retained, and N is sample number, and α is degree of confidence; F _α(A, N-A) is that to correspond to degree of confidence be α, and degree of freedom is that F under A, N-A condition distributes critical value;

The computing formula of SPE statistic is:

${\mathrm{SPE}}_{\mathrm{ik}}=e_{\mathrm{ik}}{e_{\mathrm{ik}}}^T=\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{(x_{\mathrm{ikj}}-{\hat{x}}_{\mathrm{ikj}})}^2,\mathrm{ik}=\mathrm{1,2},...,\mathrm{IK}$

Wherein represent that reconstructing (1 × J) that obtain ties up estimate vector; e _ikrepresent x _ikwith reconstruct bias vector;

The control limit of SPE statistic utilizes χ ²distribution adopts following formula to calculate:

$\mathrm{SPE}~g{\mathrm{\&chi;}}_{h,\mathrm{\&alpha;}}^2$

Wherein g=v/2n, h=2n ²/ v; N, v are respectively average and the variance of SPE statistic.

Meanwhile, to step 5) in the process data of Real-time Collection, adopt step 2) in the pretreatment mode of like products, carry out the standardization pre-service of new sampled data.

Pretreated real process data x _newthe T that (1 × J) is corresponding ²normalized set formula is as follows:

t _new＝x _newP _A

$T_{\mathrm{new}}^2=t_{\mathrm{new}}{S}^{-1}{t}_{\mathrm{new}}^T=\underset{a=1}{\overset{A}{\mathrm{\&Sigma;}}}\frac{t_{\mathrm{new},a}^2}{{\mathrm{\&lambda;}}_a}$

Wherein P _arepresent that (J × A) of corresponding product PCA monitoring model ties up load matrix, S=diag (λ ₁..., λ _a) represent corresponding product PCA monitoring model before A eigenwert (A × A) that form tie up diagonal matrix;

Pretreated real process data x _newthe SPE normalized set formula that (1 × J) is corresponding is as follows:

${\hat{x}}_{\mathrm{new}}=t_{\mathrm{new}}{P}_A^T=x_{\mathrm{new}}{P}_A{P}_A^T$

$e_{\mathrm{new}}=x_{\mathrm{new}}-{\hat{x}}_{\mathrm{new}}=x_{\mathrm{new}}(I-P_A{P}_A^T)$

${\mathrm{SPE}}_{\mathrm{new}}=e_{\mathrm{new}}{e}_{\mathrm{new}}^T=\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{(x_{\mathrm{new},j}-{\hat{x}}_{\mathrm{new},j})}^2$

Wherein represent that reconstructing (1 × J) that obtain ties up estimate vector, e _newrepresent x _newwith reconstruct bias vector.

In step 6) in, when the normal control that exceed of statistic is prescribed a time limit, a major component t _{new, a}right contribution rate be calculated as follows:

$C_{t_a}=\frac{t_{\mathrm{new},a}^2}{{\mathrm{\&lambda;}}_a}/T_{\mathrm{new}}^2,(a=1,...,A)$

Wherein λ _arepresent a eigenwert of corresponding product PCA monitoring model;

Process variable x _{ik, j}to t _{new, a}contribution rate be calculated as follows:

$C_{t_a,x_{\mathrm{ik},j}}=x_{\mathrm{ik},j}{p}_{j,a}/t_{\mathrm{new},a},(a=1,...,A;j=1,...,J)$

Wherein p _j,arepresent the load variation of corresponding product PCA monitoring model;

Work as SPE _newthe normal control that exceed of statistic is prescribed a time limit, process variable x _{ik, j}to SPE _newcontribution rate be calculated as follows:

$C_{\mathrm{SPE},x_{\mathrm{ik},j}}=\mathrm{sign}(x_{\mathrm{ik},j}-{\hat{x}}_{\mathrm{ik},j})\&CenterDot;\frac{{(x_{\mathrm{ik},j}-{\hat{x}}_{\mathrm{ik},j})}^2}{{\mathrm{SPE}}_{\mathrm{new}}}$

Wherein represent the positive negative information of residual error.

The invention has the beneficial effects as follows:

Many pca models on-line monitoring that the present invention proposes and contribution plot method for diagnosing faults are by analyzing the smoked sheet pretreatment section three-dimensional data with batch process feature, promptly and accurately monitor fault occur and review the causal variable causing unusual service condition, can dynamic between more deep announcement batch and the incidence relation change between process variable than traditional rainbow figure and univariate statistics course control method for use, and can review and determine to cause the primary process variable of unusual service condition in time, effectively improve reliability and the accuracy of smoked sheet pretreatment section detection and diagnosis result, for the maintenance of site operation personnel and service work provide scientific guidance.

Accompanying drawing explanation

Fig. 1 is smoked sheet pretreatment section on-line monitoring and fault diagonosing method flow diagram of the present invention;

Fig. 2 is the three-dimensional data expression figure of cigarette primary processing process smoked sheet pretreatment section;

Fig. 3 is the anisochronous data form figure of cigarette primary processing process smoked sheet pretreatment section;

Fig. 4 is the data analysis unit figure after launching according to attribute;

Fig. 5 is the many PCA monitoring model structural drawing based on different leaves product grade;

Fig. 6 is the T of 29 batches of modeling datas ²with SPE process monitoring figure;

Fig. 7 is the T of 9 normal batch of test datas ²with SPE process monitoring figure;

Fig. 8 is the T of loose humidity discharging negative pressure fault ²with SPE process monitoring figure;

Fig. 9 is the T of loose new air temperature fault ²with SPE process monitoring figure;

Figure 10 is the T of a front end steam membrane valve aperture fault ²with SPE process monitoring figure;

Figure 11 is the T of a front end vapor (steam) temperature fault ²with SPE process monitoring figure;

Figure 12 is that the variable contribution of loose humidity discharging negative pressure fault is transfinited rate figure;

Figure 13 is that the variable contribution of loose new air temperature fault is transfinited rate figure;

Figure 14 is that the variable contribution of a front end steam membrane valve aperture fault is transfinited rate figure;

Figure 15 is that the variable contribution of a front end vapor (steam) temperature fault is transfinited rate figure.

Embodiment

Technical scheme for a better understanding of the present invention, is further described embodiments of the present invention below in conjunction with accompanying drawing in instructions.

This enforcement is the on-line monitoring and fault diagonosing method for cigarette primary processing process smoked sheet pretreatment section, mainly for German Hauni company's T B-L loosening steam conditioner and KAS charger (German KAS charger).

The Main Function of loosening steam conditioner be by section after smoked sheet solution pine and mildly get damp again and heat, improve the resist processing of smoked sheet, improve the aesthetic quality of smoked sheet.The cylinder tilted towards throughput direction is placed on plastics active wheel, four synchronous machine drives cylinders, and drum rotation speed can by the electrodeless adjustment of frequency converter; Circulating air pipeline is connected with exhaust piping by bypass, is regulated make to draw off in cover slightly in subnormal ambient by the pressure ratio of throttling valve in bypass; A part for circulating air, by the sucking-off of circulating air pipeline, utilizes heat exchanger carry out the heating of fresh air and inputted by input channel; Steam/water mixing nozzle and a steam jet of a water amount regulator are equipped with in drum inlet place; By the rotation of cylinder, tobacco leaf is separated pine, is arranged on the pin on inner wall of rotary drum and raises plate to contribute to separating loose process; Realizing the warming and humidifying of tobacco leaf by running through the moisture and hot ageing air of cylinder, the steam being blown into cylinder input field and water, wherein utilizing moisture film plate valve to carry out the adjustment of quantity of steam; By the heating fresh air inputted especially, independent regulation is carried out to the humidity and temperature of circulating air.

A charger Main Function accurately applies feed liquid uniformly according to recipe requirements and applying ratio to smoked sheet, regulates and improve the jealous of tobacco product, enhanced burning and moisture-retaining capacity, and smoked sheet is fully absorbed.The cylinder tilted towards throughput direction is placed on plastics active wheel, four synchronous machine drives cylinders, and drum rotation speed can by the electrodeless adjustment of frequency converter; In reinforced process, tobacco leaf enters in the cylinder of rotation by the tray conveyer that shakes by the opening of on inlet shroud, the pin of drum inside makes tobacco leaf loose, and tobacco leaf continues to be installed by inclination and the cylinder rotated stirs, stirs and carry, and ensures that feed liquid is sprayed onto on tobacco leaf equably; Feed liquid is supplied by the double nozzle of cylinder input side, sprays by steam or pressurized air; Drum outlet place is provided with the steam/water mixing nozzle that water amount regulates, and carries out the quantity of steam of warming and humidifying and the water yield is regulated by diaphragm valve to the tobacco leaf in cylinder; Circulating air blower fan aspirates circulating air and is delivered to the entrance side of cylinder; The heating coil below cylinder is utilized to carry out heat supply to casing drum.

Smoked sheet pretreatment section on-line monitoring and fault diagonosing method of the present invention realize block diagram as shown in Figure 1, method is mainly divided into the following steps:

(1) the process operation data of different leaves product grade under nominal situation are obtained

If a production batch operating process of a certain leaf product trade mark has K sampled point and J measurand, then this production batch can obtain a two-dimensional data matrix X (K × J).After repeating I production batch to this product grade, the data of acquisition can be expressed as a three-dimensional data matrix x(I × J × K).

In this example, the leaf product trade mark has: sharp group (soft proboscis), sharp group (blue sky), sharp group (soft red proboscis), sharp group (Divine Land), sharp group (new edition), sharp group (leisure), sharp group (sunlight), sharp group (proboscis), sharp group (soft old version), sharp group (firmly), Sambalion (red), Sambalion (firmly), modern (No. 2) etc. 13 kinds, chooses 29 batch process service datas under sharp group (soft proboscis) the blade trade mark, the TB-L loosening steam conditioner of German Hauni company in smoked sheet pretreatment section, the process variable of the key equipments such as a KAS charger has: sheet plume amount (kg/h), loose front end adds discharge (l/h), loose steam mass flow (kg/h), loose humidity discharging negative pressure (mbar), loose hot blast temperature (DEG C), loose hot blast throttle opening (%), loose drum rotation speed (l/min), loose new wind throttle opening (%), loose new wind steam valve aperture (%), loose hot-air steam valve opening (%), loose steam membrane valve aperture (%), divide number of slices (sheet), loose new air temperature (DEG C), loose vapour volume flow (m3/h), vapor (steam) temperature (DEG C) before loose valve, vapor pressure (bar) before loose valve, loose moisture content of outlet (%), loose outlet temperature (DEG C), a liquid material flow (kg/h), one hypo-tobacco leaf flow (kg/h), once reinforced instantaneous precision (%), once reinforced instantaneous ratio (%), once vapor pressure (bar) before front end valve, once cylinder rotating speed (l/min), front end steam membrane valve aperture (%), a barrel temperature (DEG C), front end vapour volume flow (m3/h), front end vapor (steam) temperature (DEG C), a front end steam mass flow (kg/h), a moisture content of outlet (%), an outlet temperature (DEG C) etc. 31.

Sampling should be carried out every 10 seconds for process variable, adopts mean trajectory thought to do arithmetic mean to continuous 6 data and obtain valid data, finally obtains three-dimensional modeling data matrix x(29 × 31 × K _i), cause the sampled data Length discrepancy between different batches because the running time is uncertain, the sampled point of i-th batch is K _i, as shown in Figure 2.

(2) two-dimentional modeling data and data prediction is obtained according to attribute expansion mode

By the analysis on Operating of smoked sheet pretreatment section, only a steady working condition is there is in same batch of a certain product grade, mutual relationship between explanatory variable has identical process feature, cause the sampled data Length discrepancy between different batches because the running time is uncertain simultaneously, as shown in Figure 3, the factor considering these two aspects adopts three-dimensional data attribute expansion mode, by having batch, the process variable data matrix of time and attribute three dimensional characteristic x(I × J × K) is launched into two-dimensional matrix X (IK × J), as shown in Figure 4.

In this example, three-dimensional data matrix is x(29 × 31 × K _i), two-dimentional modeling data matrix X (2656 × 31) can be obtained according to attribute expansion mode, effectively prevent the inapplicable problem of statistical modeling method that sampled data Length discrepancy brings, without the need to whole lot data thus without the need to estimating future time instance data during application on site.

If the variable of two-dimensional matrix X (2656 × 31) interior any point is x _{ik, j}, subtract average, data normalization pre-service except standard deviation to this variable, wherein subscript i representative batch, j represents variable, k represents sampled point, and the computing formula of standardization is:

${\tilde{x}}_{\mathrm{ik},j}=\frac{x_{\mathrm{ik},j}-{\stackrel{\&OverBar;}{x}}_j}{s_j},i=\mathrm{1,2},...,29;j=\mathrm{1,2},...,31$

${\stackrel{\&OverBar;}{x}}_j=\frac{1}{2656}\underset{k=1}{\overset{K_i}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{29}{\mathrm{\&Sigma;}}}{x}_{\mathrm{ik},j},s_j=\sqrt{\frac{1}{2655}\underset{k=1}{\overset{K_i}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{29}{\mathrm{\&Sigma;}}}{(x_{\mathrm{ik},j}-{\stackrel{\&OverBar;}{x}}_j)}^2}$

Data after standardization give prominence to the change of process variable measurement on time orientation, and in same batch, only there is a steady working condition, therefore this average and variance represent average level and the degree of fluctuation of process operation.

(3) PCA decomposition is carried out to modeling data, set up many PCA monitoring model

To (IK × J) dimension data matrix X=[x under each product grade ₁, x ₂..., x _ik..., x _iK] carry out PCA decomposition, X=[x ₁, x ₂..., x _ik..., x _iK] in each element, represent wherein a line of pretreated two-dimensional matrix, set up many PCA monitoring model, as shown in Figure 5, PCA decompose computing formula be:

$X={\mathrm{TP}}^T=\underset{r=1}{\overset{J}{\mathrm{\&Sigma;}}}{t}_r{p}_r^T$

Wherein t _rrepresent the orthogonal principal component vector that (IK × 1) ties up, p _rrepresent the orthonomalization load vector that (J × 1) ties up, r represents that different PCA decomposes direction, the transposition of subscript T representing matrix, (IK × J) that T representative retains whole pivot ties up score matrix, and P represents corresponding (J × J) and ties up load matrix.

λ ₁, λ ₂..., λ _jfor the covariance matrix Σ X of modeling data collection X ^tthe All Eigenvalues of X, retain the fluctuation information of in original data space more than 90%, then the pivot number A retained in pca model can be obtained by subordinate's formulae discovery:

$\frac{\underset{j=1}{\overset{A}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_j}{\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_j}\&GreaterEqual;0\%$

The computing formula that PCA decomposes can become following form by re:

$X=T_A{P_A}^T+E=\underset{a=1}{\overset{A}{\mathrm{\&Sigma;}}}{t}_a{p}_a^T+E$

Wherein a represents that different PCA decomposes direction; T _arepresent that (IK × A) after retaining A pivot ties up score matrix, P _arepresent that (J × A) after retaining A pivot ties up load matrix, E is residual matrix, t _arepresent (IK × 1) dimension score vector, p _arepresent (J × 1) dimension load vector.By above-mentioned conversion, original data space is decomposed into principal component space and residual error space, represents main process variation information in principal component space, pivot number A retained here can reflect the process variation information of in former process 90%.

In this example, PCA monitoring model only needs 13 pivots just can explain the fluctuation information of 90%.

(4) T of each monitoring model of calculated off-line ²with SPE statistic index and control limit

Based on normal batch of modeling data of each PCA monitoring model, the Hotelling-T of calculated off-line major component subspace ²with the SPE statistic of residual error subspace, wherein T ²statistic index features the degree that each major component departs from model in variation tendency and amplitude, and SPE statistic index features the departure degree of measured value to principal component model of input variable.

T ²the computing formula of statistic is:

T ² _ik＝t _ikS ^-1t _ik ^Tik＝1,2,…,IK

Wherein t _ik=x _ikp _arepresent the pivot score vector that (1 × J) ties up, diagonal matrix S=diag (λ ₁..., λ _a) be by the covariance matrix Σ X of modeling data collection X ^tfront A the eigenwert of X formed.

T ²the control limit of statistic can utilize F to distribute and adopt following formula calculating:

$T^2~\frac{A(N-1)}{N-A}{F}_{\mathrm{\&alpha;}}(A,N-A)$

Wherein A is the pivot number retained, and N is sample number; α is significance, F _α(A, N-A) is that to correspond to insolation level be α, and degree of freedom is that F under A, N-A condition distributes critical value.

The computing formula of SPE statistic is:

${\mathrm{SPE}}_{\mathrm{ik}}=e_{\mathrm{ik}}{e_{\mathrm{ik}}}^T=\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{(x_{\mathrm{ikj}}-{\hat{x}}_{\mathrm{ikj}})}^2,\mathrm{ik}=\mathrm{1,2},...,\mathrm{IK}$

Wherein represent that reconstructing (1 × J) that obtain ties up estimate vector; e _ikrepresent x _ikwith reconstruct bias vector.

The control limit of SPE statistic can utilize χ ²distribution adopts following formula to calculate:

$\mathrm{SPE}~g{\mathrm{\&chi;}}_{h,\mathrm{\&alpha;}}^2$

Wherein g=v/2n, h=2n ²/ v; N, v are respectively average and the variance of SPE statistic.

In this example, the degree of confidence of Statisti-cal control limit is 0.99, T ²the control that the control of statistic is limited to 27.9124, SPE statistic is limited to 7.6031, the T of modeling data ²with SPE process monitoring result as shown in Figure 6.

(5) based on the online process monitoring of many PCA monitoring model

During online process monitoring, gather the new process measurement data x of current time _new(1 × J), the identical trade mark data mean value obtained according to current production trade mark invocation step (2) and standard deviation, carry out the standardization pre-service of new sampled data.At line computation data x _newcorresponding T ²with SPE statistical indicator, itself and control limit are compared, whether deterministic process and equipment there is exception.In this example, new sampled data is 31 process variable in smoked sheet pretreatment section.

In line computation monitoring and statistics amount computing formula is as follows:

t _new＝x _newP _A

$T_{\mathrm{new}}^2=t_{\mathrm{new}}{S}^{-1}{t}_{\mathrm{new}}^T=\underset{a=1}{\overset{A}{\mathrm{\&Sigma;}}}\frac{t_{\mathrm{new},a}^2}{{\mathrm{\&lambda;}}_a}$

Wherein P _arepresent that (J × A) of corresponding product trade mark PCA monitoring model ties up load matrix, S=diag (λ ₁..., λ _a) represent corresponding product trade mark PCA monitoring model before A eigenwert (A × A) that form tie up diagonal matrix.

At line computation SPE _newmonitoring and statistics amount computing formula is as follows:

${\hat{x}}_{\mathrm{new}}=t_{\mathrm{new}}{P}_A^T=x_{\mathrm{new}}{P}_A{P}_A^T$

$e_{\mathrm{new}}=x_{\mathrm{new}}-{\hat{x}}_{\mathrm{new}}=x_{\mathrm{new}}(I-P_A{P}_A^T)$

${\mathrm{SPE}}_{\mathrm{new}}=e_{\mathrm{new}}{e}_{\mathrm{new}}^T=\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{(x_{\mathrm{new},j}-{\hat{x}}_{\mathrm{new},j})}^2$

Wherein represent that reconstructing (1 × J) that obtain ties up estimate vector, e _newrepresent x _newwith reconstruct bias vector.

In this example, choose 9 normal batch process service datas under sharp group (soft proboscis) the leaf product trade mark as test data matrix x(9 × 31 × K _i), launch to obtain two-dimensional matrix X (813 × 31), the T of test data by three-dimensional data attribute ²with SPE process monitoring result as shown in Figure 7.As comparing, again 2 normal batch process service datas are chosen, when the 51st sample point, make successively loose humidity discharging negative pressure, loose new air temperature, a front end steam membrane valve aperture, a front end vapor (steam) temperature value increase suddenly, recover normal when being continued until the 90th sample point, T ²as shown in Figs. 8 to 11 respectively with SPE process monitoring result.

(6) the unusual service condition causal variable based on variable contribution plot is determined

Control in limited time, to calculate the contribution margin of each process variable to the statistic that transfinites, find out the primary process variable causing process and unit exception when monitoring and statistics amount exceeds.When major component subspace the normal control that exceed of monitoring and statistics amount is prescribed a time limit, a major component t _{new, a}right contribution rate can be calculated as follows:

$C_{t_a}=\frac{t_{\mathrm{new},a}^2}{{\mathrm{\&lambda;}}_a}/T_{\mathrm{new}}^2,(a=1,...,A)$

Wherein λ _arepresent a eigenwert of corresponding product trade mark PCA monitoring model.

Process variable x _{ik, j}to t _{new, a}contribution rate can be calculated as follows:

$C_{t_a,x_{\mathrm{ik},j}}=x_{\mathrm{ik},j}{p}_{j,a}/t_{\mathrm{new},a},(a=1,...,A;j=1,...,J)$

Wherein p _j,arepresent the load variation of corresponding product trade mark PCA monitoring model.

As residual error subspace SPE _newthe normal control that exceed of monitoring and statistics amount is prescribed a time limit, process variable x _{ik, j}to SPE _newcontribution rate can be calculated as follows:

$C_{\mathrm{SPE},x_{\mathrm{ik},j}}=\mathrm{sign}(x_{\mathrm{ik},j}-{\hat{x}}_{\mathrm{ik},j})\&CenterDot;\frac{{(x_{\mathrm{ik},j}-x_{\mathrm{ik},j})}^2}{{\mathrm{SPE}}_{\mathrm{new}}}$

Wherein represent the positive negative information of residual error.

In this example, under calculating loose humidity discharging negative pressure, loose new air temperature, a front end steam membrane valve aperture, a front end vapor (steam) temperature fault respectively, each variable is to T ²with the contribution rate of SPE, analyze the primary process variable of causing trouble, the rate that transfinites of variable contribution is as shown in Figure 12 to Figure 15.

Claims (10)

1. a cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method, is characterized in that, comprise step:

1) periodically the process variable of the loosening and gaining moisture in smoked sheet pretreatment section and charging (feeding) equipment is sampled in a production batch, obtain sampling matrix X (K × J), K is sampled point number, J is monitored parameters number, after repeating I production batch, obtain corresponding three-dimensional modeling data matrix x(I × J × K _i), K _iit is the sampled point number of i-th production batch;

2) by described three-dimensional modeling data matrix x(I × J × K _i) be launched into two-dimensional matrix according to attribute, and pre-service is carried out to each element in two-dimensional matrix, obtain pretreated two-dimensional matrix;

3) PCA decomposition is carried out to pretreated two-dimensional matrix, set up the PCA monitoring model for current production;

4) T of each PCA monitoring model is calculated ²limit with the control of SPE statistic and correspondence;

5) the process data x of Real-time Collection loosening and gaining moisture and charging (feeding) equipment _new(1 × J), calls corresponding PCA monitoring model according to current production type, calculates data x _newthe T that (1 × J) is corresponding ²statistic and SPE statistic;

6) as data x _newthe T that (1 × J) is corresponding ²statistic and SPE statistic exceed corresponding control in limited time, and computation process variable is to T ²the contribution rate of statistic and SPE statistic, determines the primary process variable causing unusual service condition.

2. cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as claimed in claim 1, is characterized in that, in step 1) in, to the smoothing process of process variable of continuous multiple sampled point, the three-dimensional modeling data matrix described in formation.

3. cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as claimed in claim 2, it is characterized in that, described smoothing processing is do arithmetic mean to the continuous several times sampled data of a certain process variable to obtain valid data.

4. cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as claimed in claim 1, is characterized in that, step 2) in launch after two-dimensional matrix be X (IK _i× J).

5. the cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as described in claim 1 or 4, is characterized in that, step 2) in pre-service comprise carry out successively subtract average, except standard deviation process.

6. cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as claimed in claim 1, is characterized in that, in step 3) in, the same row element of pretreated two-dimensional matrix is considered as modeling data collection X=[x ₁, x ₂..., x _ik..., x _iK], carry out PCA decomposition to X, the computing formula that PCA decomposes is as follows:

$X=T_A{P_A}^T+E=\underset{a=1}{\overset{A}{\mathrm{\&Sigma;}}}{t}_a{p}_a^T+E$

Wherein, A is pivot number, and a represents that different PCA decomposes direction, T _arepresent that (IK × A) after retaining A pivot ties up score matrix, P _arepresent that (J × A) after retaining A pivot ties up load matrix, E is residual matrix.

7. the cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as described in claim 1 or 6, is characterized in that, described step 4) in, T ²the computing formula of statistic is:

T ² _ik＝t _ikS ^-1t _ik ^Tik＝1,2,…,IK

Wherein t _ik=x _ikp _arepresent the pivot score vector that (1 × J) ties up, diagonal matrix S=diag (λ ₁..., λ _a) be by the covariance matrix Σ X of modeling data collection X ^tfront A the eigenwert of X formed;

T ²the control limit of statistic utilizes F to distribute and adopts following formula calculating:

$T^2~\frac{A(N-1)}{N-A}{F}_{\mathrm{\&alpha;}}(A,N-A)$

Wherein A is the pivot number retained, and N is sample number, and α is degree of confidence; F _α(A, N-A) is that to correspond to degree of confidence be α, and degree of freedom is that F under A, N-A condition distributes critical value;

The computing formula of SPE statistic is:

${\mathrm{SPE}}_{\mathrm{ik}}=e_{\mathrm{ik}}{e_{\mathrm{ik}}}^T=\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{(x_{\mathrm{ikj}}-{\hat{x}}_{\mathrm{ikj}})}^2\mathrm{ik}=\mathrm{1,2},...,\mathrm{IK}$

Wherein represent that reconstructing (1 × J) that obtain ties up estimate vector; e _ikrepresent x _ikwith reconstruct bias vector;

The control limit of SPE statistic utilizes χ ²distribution adopts following formula to calculate:

$\mathrm{SPE}~{\mathrm{g\&chi;}}_{h,\mathrm{\&alpha;}}^2$

Wherein g=v/2n, h=2n ²/ v; N, v are respectively average and the variance of SPE statistic.

8. cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as claimed in claim 7, it is characterized in that, to step 5) in the process data of Real-time Collection, adopt step 2) in the pretreatment mode of like products, carry out the standardization pre-service of new sampled data.

9. cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as claimed in claim 8, is characterized in that, pretreated real process data x _newthe T that (1 × J) is corresponding ²normalized set formula is as follows:

t _new＝x _newP _A

$T_{\mathrm{new}}^2=t_{\mathrm{new}}{S}^{-1}{t}_{\mathrm{new}}^T=\underset{a=1}{\overset{A}{\mathrm{\&Sigma;}}}\frac{t_{\mathrm{new},a}^2}{{\mathrm{\&lambda;}}_a}$

Wherein P _arepresent that (J × A) of corresponding product PCA monitoring model ties up load matrix, S=diag (λ ₁..., λ _a) represent corresponding product PCA monitoring model before A eigenwert (A × A) that form tie up diagonal matrix;

Pretreated real process data x _newthe SPE normalized set formula that (1 × J) is corresponding is as follows:

${\hat{x}}_{\mathrm{new}}=t_{\mathrm{new}}{P}_A^T=x_{\mathrm{new}}{P}_A{P}_A^T$

$e_{\mathrm{new}}=x_{\mathrm{new}}-{\hat{x}}_{\mathrm{new}}=x_{\mathrm{new}}(I-P_A{P}_A^T)$

${\mathrm{SPE}}_{\mathrm{new}}=e_{\mathrm{new}}{e}_{\mathrm{new}}^T=\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{(x_{\mathrm{new},j}-{\hat{x}}_{\mathrm{new},j})}^2$

Wherein represent that reconstructing (1 × J) that obtain ties up estimate vector, e _newrepresent x _newwith reconstruct bias vector.

10. cigarette primary processing process smoked sheet pretreatment section on-line monitoring and fault diagonosing method as claimed in claim 9, is characterized in that, when the normal control that exceed of statistic is prescribed a time limit, a major component t _{new, a}right contribution rate be calculated as follows:

$C_{t_a}=\frac{t_{\mathrm{new},a}^2}{{\mathrm{\&lambda;}}_a}/T_{\mathrm{new}}^2(a=1,...,4)$

Wherein λ _arepresent a eigenwert of corresponding product PCA monitoring model;

Process variable x _{ik, j}to t _{new, a}contribution rate be calculated as follows:

$C_{t_a,x_{\mathrm{ik},j}}=x_{\mathrm{ik},j}{p}_{j,a}/t_{\mathrm{new},a}(a=1,...,A;j=1,...,J)$

Wherein p _j,arepresent the load variation of corresponding product PCA monitoring model;

Work as SPE _newthe normal control that exceed of statistic is prescribed a time limit, process variable x _{ik, j}to SPE _newcontribution rate be calculated as follows:

$C_{{\mathrm{SPE},x}_{\mathrm{ik},j}}=\mathrm{sign}(x_{\mathrm{ik},j}-{\hat{x}}_{\mathrm{ik},j})\&CenterDot;\frac{{(x_{\mathrm{ik},j}-{\hat{x}}_{\mathrm{ik},j})}^2}{{\mathrm{SPE}}_{\mathrm{new}}}$

Wherein represent the positive negative information of residual error.