CN114358060B - Crane equipment fault detection method - Google Patents
Crane equipment fault detection method Download PDFInfo
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- CN114358060B CN114358060B CN202111569683.9A CN202111569683A CN114358060B CN 114358060 B CN114358060 B CN 114358060B CN 202111569683 A CN202111569683 A CN 202111569683A CN 114358060 B CN114358060 B CN 114358060B
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- 238000001514 detection method Methods 0.000 title claims abstract description 16
- 239000000284 extract Substances 0.000 claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims description 21
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- 230000000007 visual effect Effects 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 3
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Abstract
The invention relates to a crane equipment fault detection method, which comprises the following steps: step one, when a mechanical component X 0 fails, a vibration sensor Y 0 collects vibration signals of the mechanical component X 0 and uploads the vibration signals to an upper computer; the upper computer extracts the average vibration frequency of the vibration signalExtracting features of the vibration signals to form a feature value set G; defining a mechanical component X 0 fault vibration frequency interval f 'and a characteristic value interval G'; step two, during operation, the vibration sensor Y 0 collects a vibration signal e p of the mechanical component X 0 and uploads the vibration signal e p to the upper computer; step three, the upper computer filters and reduces noise of the vibration signal e p, and then extracts the vibration frequency f p; if the judgment result f p falls into the fault vibration frequency interval f', entering a step four; step four, extracting a characteristic value g p of the vibration signal e p; if G p falls within the failure characteristic value interval G', it is determined that the machine component X 0 is failed. The invention judges whether the crane has mechanical failure or not by detecting the vibration signal of the crane.
Description
Technical Field
The invention relates to the technical field of crane equipment detection and maintenance, in particular to a crane equipment fault detection method.
Background
The crane is a multi-action hoisting machine for vertically lifting and horizontally carrying heavy objects in a certain range, and is also called crown block, aerial crane, crane and the like. Because the crane has large equipment volume and high difficulty in daily fault detection, a great deal of time is spent in finding out fault types after faults occur. Therefore, how to quickly check and find the type of crane fault is an important task.
The types and causes of crane failures are numerous, with mechanical component failures of the crane being one of the common causes. In the actual fault maintenance working process of crane equipment, when a mechanical component of a general crane is in fault, the vibration signal of the crane is often changed, but the vibration signal change is difficult to sense through the self-sensing capability of a person. Based on the method, the invention designs a crane equipment fault detection method, and whether mechanical faults occur to mechanical parts of the crane can be accurately judged by detecting the vibration signals of the mechanical parts of the crane.
Disclosure of Invention
Therefore, in order to solve the above-mentioned problems, the present invention provides a method for detecting a crane equipment failure, which detects a vibration signal of a crane to determine whether the crane has a mechanical failure.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a crane equipment fault detection method comprises the following steps:
Training a crane equipment fault analysis model, which comprises the following substeps:
a. A vibration sensor Y 0 for acquiring vibration signals is arranged at the position of a mechanical component X 0 of the crane equipment;
the vibration sensor Y 0 is connected to the upper computer;
b. When the mechanical component X 0 fails, the vibration sensor Y 0 collects vibration signals of the mechanical component X 0 for a plurality of times, and the vibration signals are respectively e 1、e2…en and are uploaded to an upper computer;
c. The upper computer filters and reduces noise of the vibration signals e 1、e2…en respectively and extracts vibration frequencies f 1、f2…fn corresponding to the vibration signals e 1、e2…en one by one;
The average vibration frequency of the upper computer computing machine component X 0 when the fault occurs is
Defining a fault vibration frequency interval of the mechanical component X 0
0＜δ≤0.1；
D. After the upper computer filters and reduces noise of the vibration signals e 1、e2…en respectively, feature extraction is carried out on the vibration signals e 1、e2…en respectively to form a feature value set G= { G 1、g2…gn };
Defining a fault characteristic value interval G' = [ G a,gb ] of the mechanical component X 0; the G a is the minimum value of the elements in the set g= { G 1、g2…gn } and the G b is the maximum value of the elements in the set g= { G 1、g2…gn };
Step two, when the crane equipment normally operates, the vibration sensor Y 0 acquires a vibration signal e p of the mechanical component X 0 every a period of time and uploads the vibration signal e p to an upper computer;
Step three, the upper computer filters and reduces noise of a vibration signal e p, and then extracts the vibration frequency f p of the vibration signal;
If the f p does not fall into the fault vibration frequency interval f', the upper computer judges that the mechanical component X 0 operates normally;
If f p falls into the fault vibration frequency interval f', entering a step four;
Step four, the upper computer performs feature extraction on the vibration signal e p to form a feature value g p;
If G p does not fall into the fault characteristic value interval G', the upper computer judges that the mechanical component X 0 operates normally;
If G p falls within the failure characteristic value interval G', the upper computer judges that the mechanical component X 0 fails.
Further, the feature extraction method adopts any one of time-frequency analysis, frequency domain analysis, amplitude domain analysis and time domain analysis.
Further, the upper computer is connected with an industrial touch screen, and the industrial touch screen is electrically connected with the upper computer;
The industrial touch screen is used for displaying that the operation state of the mechanical component X 0 is normal or fault;
the industrial touch screen is also used to set the value of δ.
Further, the upper computer is connected with an audible and visual alarm, and the audible and visual alarm is electrically connected with the upper computer;
when the upper computer judges that the mechanical component X 0 fails, the upper computer controls the audible and visual alarm to send out an alarm.
By adopting the technical scheme, the invention has the beneficial effects that: according to the crane equipment fault detection method, the vibration sensor Y 0 is used for collecting a mechanical component X 0 fault vibration signal of the crane, so that a crane equipment fault analysis model is trained. Specifically, the upper computer extracts the average vibration frequency of the vibration signalExtracting features of the vibration signals to form a feature value set G; a mechanical component X 0 failure vibration frequency interval f 'and a eigenvalue interval G' are defined.
When the crane equipment is in operation, the vibration sensor Y 0 collects vibration signals e p of the mechanical component X 0 at intervals and uploads the vibration signals e p to the upper computer, and the upper computer filters and reduces noise of the vibration signals e p and extracts vibration frequency f p; if the judgment f p falls into the fault vibration frequency interval f 'and the judgment G p falls into the fault characteristic value interval G', the upper computer judges that the mechanical component X 0 is in fault.
If the judgment f p does not fall into the fault vibration frequency interval f', the upper computer judges that the mechanical component X 0 is normal. Or although f p falls within the failure vibration frequency interval f ', G p does not fall within the failure characteristic value interval G', and the upper computer judges that the mechanical component X 0 is normal.
The crane equipment fault detection method has high accuracy in detecting the faults of the mechanical components X 0.
Detailed Description
The invention will now be further described with reference to specific embodiments.
The embodiment provides a crane equipment fault detection method, which is characterized by comprising the following steps:
Training a crane equipment fault analysis model, which comprises the following substeps:
a. A vibration sensor Y 0 for acquiring vibration signals is arranged at the position of a mechanical component X 0 of the crane equipment;
The vibration sensor Y 0 is connected to the upper computer, and the upper computer is also connected with an industrial touch screen and an audible and visual alarm;
the upper computer, the industrial touch screen and the audible and visual alarm are all existing electronic equipment.
B. When the mechanical component X 0 fails, the vibration sensor Y 0 collects vibration signals of the mechanical component X 0 for a plurality of times, and the vibration signals are respectively e 1、e2…en and are uploaded to an upper computer;
c. The upper computer filters and reduces noise of the vibration signals e 1、e2…en respectively and extracts vibration frequencies f 1、f2…fn corresponding to the vibration signals e 1、e2…en one by one;
In this embodiment, the filtering and noise reduction method uses a wavelet noise reduction method to implement multi-resolution decomposition on the vibration signal (vibration signal e 1、e2…en), and performs investigation and analysis on the characteristics of the vibration signal through multiple layers. The wavelet noise reduction method is a well known technology in the art, and is specifically described in journal of machinery and electronics published in month 1 of 2021, volume 39, first-stage published in WA-ESN-based construction hoisting machinery fault detection, and detailed description thereof is omitted herein.
The average vibration frequency of the upper computer computing machine component X 0 when the fault occurs is
Defining a fault vibration frequency interval of the mechanical component X 0
Delta is more than 0 and less than or equal to 0.1; the value of delta is set by an industrial touch screen. In this specific embodiment, the value of δ is set to 0.5.
I.e.
D. After the upper computer filters and reduces noise of the vibration signals e 1、e2…en respectively, feature extraction is carried out on the vibration signals e 1、e2…en respectively to form a feature value set G= { G 1、g2…gn };
the filtering and noise reduction method in the step also adopts a wavelet noise reduction method.
The feature extraction method in the step adopts a time-frequency analysis method, and the time-frequency analysis method provides joint distribution information of a time domain and a frequency domain, so that the relation of the change of signal frequency along with time is clearly described. The time-frequency analysis method is a means known in the art, and is not described in detail herein.
Defining a fault characteristic value interval G' = [ G a,gb ] of the mechanical component X 0; the G a is the minimum value of the elements in the set g= { G 1、g2…gn } and the G b is the maximum value of the elements in the set g= { G 1、g2…gn }.
And step two, when the crane equipment normally operates, the vibration sensor Y 0 acquires a vibration signal e p of the mechanical component X 0 every a period of time and uploads the vibration signal e p to the upper computer.
Step three, the upper computer filters and reduces noise of the vibration signal e p, and then extracts the vibration frequency f p of the vibration signal;
the filtering and noise reduction method in the step also adopts a wavelet noise reduction method.
The feature extraction method in the step adopts a time-frequency analysis method.
If the f p does not fall into the fault vibration frequency interval f', the upper computer judges that the mechanical component X 0 operates normally;
If f p falls within the fault vibration frequency interval f', step four is entered.
Step four, the upper computer performs feature extraction on the vibration signal e p to form a feature value g p;
the feature extraction method in the step adopts a time-frequency analysis method.
If G p does not fall into the fault characteristic value interval G', the upper computer judges that the mechanical component X 0 operates normally;
If G p falls within the failure characteristic value interval G', the upper computer judges that the mechanical component X 0 fails.
The industrial touch screen is used for displaying that the operation state of the mechanical component X 0 is normal or fault; when the upper computer judges that the mechanical component X 0 is in failure, the industrial touch screen displays that the mechanical component X 0 is in failure, and the upper computer controls the audible and visual alarm to send out an alarm.
The method for extracting the characteristics can also adopt frequency domain analysis, amplitude domain analysis or time domain analysis.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. The crane equipment fault detection method is characterized by comprising the following steps of:
Training a crane equipment fault analysis model, which comprises the following substeps:
a. A vibration sensor Y 0 for acquiring vibration signals is arranged at the position of a mechanical component X 0 of the crane equipment;
the vibration sensor Y 0 is connected to the upper computer;
b. When the mechanical component X 0 fails, the vibration sensor Y 0 collects vibration signals of the mechanical component X 0 for a plurality of times, and the vibration signals are respectively e 1、e2…en and are uploaded to an upper computer;
c. The upper computer filters and reduces noise of the vibration signals e 1、e2…en respectively and extracts vibration frequencies f 1、f2…fn corresponding to the vibration signals e 1、e2…en one by one;
The average vibration frequency of the upper computer computing machine component X 0 when the fault occurs is
Defining a fault vibration frequency interval of the mechanical component X 0
0＜δ≤0.1；
D. After the upper computer filters and reduces noise of the vibration signals e 1、e2…en respectively, feature extraction is carried out on the vibration signals e 1、e2…en respectively to form a feature value set G= { G 1、g2…gn };
Defining a fault characteristic value interval G' = [ G a,gb ] of the mechanical component X 0; the G a is the minimum value of the elements in the set g= { G 1、g2…gn } and the G b is the maximum value of the elements in the set g= { G 1、g2…gn };
Step two, when the crane equipment normally operates, the vibration sensor Y 0 acquires a vibration signal e p of the mechanical component X 0 every a period of time and uploads the vibration signal e p to an upper computer;
Step three, the upper computer filters and reduces noise of a vibration signal e p, and then extracts the vibration frequency f p of the vibration signal;
If the f p does not fall into the fault vibration frequency interval f', the upper computer judges that the mechanical component X 0 operates normally;
If f p falls into the fault vibration frequency interval f', entering a step four;
Step four, the upper computer performs feature extraction on the vibration signal e p to form a feature value g p;
If G p does not fall into the fault characteristic value interval G', the upper computer judges that the mechanical component X 0 operates normally;
If G p falls within the failure characteristic value interval G', the upper computer judges that the mechanical component X 0 fails.
2. The crane equipment failure detection method according to claim 1, characterized in that: the feature extraction method adopts any one of time-frequency analysis, frequency domain analysis, amplitude domain analysis and time domain analysis.
3. A crane equipment failure detection method according to claim 1 or 2, characterized in that: the upper computer is connected with an industrial touch screen, and the industrial touch screen is electrically connected with the upper computer;
The industrial touch screen is used for displaying that the operation state of the mechanical component X 0 is normal or fault;
the industrial touch screen is also used to set the value of δ.
4. A crane equipment failure detection method according to claim 3, characterized in that: the upper computer is connected with an audible and visual alarm, and the audible and visual alarm is electrically connected with the upper computer;
when the upper computer judges that the mechanical component X 0 fails, the upper computer controls the audible and visual alarm to send out an alarm.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2878565A1 (en) * | 2013-11-28 | 2015-06-03 | Siemens Aktiengesellschaft | Method for determining at least one pendulum angle and/or angle of rotation of a load attached to a crane with at least one rope-based fastening and method for damping the pendular movements and/or rotary movements of the load |
| CN106197996A (en) * | 2016-06-24 | 2016-12-07 | 南京理工大学 | Offshore crane Fault Diagnosis of Gear Case device and method based on multivariate data |
| CN109052181A (en) * | 2018-10-31 | 2018-12-21 | 中船第九设计研究院工程有限公司 | A kind of shipbuilding gantry crane failure monitoring diagnostic system and method |
| CN109556895A (en) * | 2018-10-29 | 2019-04-02 | 东北大学 | The failure analysis methods and device of rotating machinery |
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2021
- 2021-12-21 CN CN202111569683.9A patent/CN114358060B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2878565A1 (en) * | 2013-11-28 | 2015-06-03 | Siemens Aktiengesellschaft | Method for determining at least one pendulum angle and/or angle of rotation of a load attached to a crane with at least one rope-based fastening and method for damping the pendular movements and/or rotary movements of the load |
| CN106197996A (en) * | 2016-06-24 | 2016-12-07 | 南京理工大学 | Offshore crane Fault Diagnosis of Gear Case device and method based on multivariate data |
| CN109556895A (en) * | 2018-10-29 | 2019-04-02 | 东北大学 | The failure analysis methods and device of rotating machinery |
| CN109052181A (en) * | 2018-10-31 | 2018-12-21 | 中船第九设计研究院工程有限公司 | A kind of shipbuilding gantry crane failure monitoring diagnostic system and method |
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