CN113450827A - Equipment abnormal condition voiceprint analysis algorithm based on compressed neural network - Google Patents
Equipment abnormal condition voiceprint analysis algorithm based on compressed neural network Download PDFInfo
- Publication number
- CN113450827A CN113450827A CN202110697792.2A CN202110697792A CN113450827A CN 113450827 A CN113450827 A CN 113450827A CN 202110697792 A CN202110697792 A CN 202110697792A CN 113450827 A CN113450827 A CN 113450827A
- Authority
- CN
- China
- Prior art keywords
- equipment
- voiceprint
- data
- abnormal
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 49
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 41
- 238000004458 analytical method Methods 0.000 title claims abstract description 24
- 238000013507 mapping Methods 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 9
- 238000013135 deep learning Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000011176 pooling Methods 0.000 claims description 2
- 230000006835 compression Effects 0.000 claims 1
- 238000007906 compression Methods 0.000 claims 1
- 238000001514 detection method Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
- G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Signal Processing (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
A voiceprint analysis algorithm for abnormal working conditions of equipment based on a compressed neural network comprises a voiceprint analysis system for the abnormal working conditions of the equipment, video data and variable data of the running conditions of the equipment; the equipment abnormal condition voiceprint analysis system comprises: the voice print data base of the normal equipment and the voice print data of the abnormal equipment, the equipment audio acquisition unit acquires the running audio of the equipment in real time and performs audio mapping on the acquired audio through an audio analysis tool; the voiceprint matching unit performs voiceprint matching on the voiceprint of the running equipment, and monitors the running state of the equipment in real time: when the operating voiceprint of the equipment is consistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal: updating the voice print of the equipment operation to a normal equipment voice print database in real time; and when the operating voiceprint of the equipment is inconsistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal and sending out an early warning signal.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence detection of data, and particularly relates to a voiceprint analysis algorithm for abnormal working conditions of equipment based on a compressed neural network.
Background
With the rapid development of artificial intelligence technology and the increase of computer hardware computing power, image processing systems with deep neural network technology as a core have come into existence, and deep learning models represented by deep neural networks have excellent performance in tasks such as target object detection and tracking, action recognition and the like, and are widely applied. The algorithm based on the deep neural network is increasingly applied to the field of intelligent detection, but most of the existing algorithms detect the appearance of parts at present, the processing object of the algorithm is an image, and the used network structure is a convolutional neural network. For products that are not visually identifiable as defective, the image-based approach described above cannot be used. In an actual production process, a worker can perform defect detection by recognizing abnormal sounds when an engine is operated, but in the field of computers, a technology for detecting defects of products by audio is still blank.
Most of the existing abnormal sound detection algorithms recognize the characteristics of abnormal sounds manually and summarize a set of algorithm flows to judge unknown sounds. The algorithm cannot automatically learn the characteristics of abnormal sounds, so that the application range is small, and the algorithm cannot be repeatedly used for different types of sound detection; some algorithms adopt a convolutional neural network to process audio, so that although the characteristics of abnormal sounds can be learned and detected by self, the convolutional neural network requires that the input has the same size, and the audio cannot meet the requirement generally; if the audio is pre-processed by cropping or the like, part of the information may be lost.
Based on the method, the voiceprint analysis algorithm of the abnormal working condition of the equipment based on the compressed neural network is designed to solve the problem that certain faults in the assembled equipment cannot be identified through observation.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a voiceprint analysis algorithm for equipment under abnormal working conditions based on a compressed neural network, and combine an extraction algorithm for key fragments of the voiceprint of the equipment and an abnormal sound detection algorithm for deep learning to solve the problems in the background technology.
In order to solve the technical problem, the invention is realized by the following modes:
a voiceprint analysis algorithm for abnormal working conditions of equipment based on a compressed neural network comprises a voiceprint analysis system for the abnormal working conditions of the equipment, video data and variable data of the running conditions of the equipment;
the equipment abnormal condition voiceprint analysis system comprises:
the normal equipment voiceprint database is internally stored with voiceprint atlas data of equipment in normal operation;
the abnormal equipment voiceprint database stores voiceprint atlas data of the abnormal operation of the equipment;
the equipment audio acquisition unit acquires equipment running audio in real time by using an online sound collection tool and performs audio mapping on the acquired audio through an audio analysis tool;
the voiceprint matching unit is used for carrying out voiceprint matching on the voiceprint of the equipment operation based on the normal equipment voiceprint database, and monitoring the equipment operation state in real time:
when the operating voiceprint of the equipment is consistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal: updating the voice print of the equipment operation to a normal equipment voice print database in real time; when the operating voiceprint of the equipment is inconsistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal and sending out an early warning signal;
the video data are input into a network model architecture composed of a compressed neural network and a fully connected neural network, the variable data are directly input into the fully connected neural network to obtain the identification results of the video data and the variable data, and after deep learning is carried out on the basis of big data composed of the video data and the variable data, the parameter setting of the network model architecture is completed to obtain a target network model architecture; the method comprises the steps of obtaining video data to be detected and variable data to be detected, inputting the video data to be detected and the variable data to be detected into a target network model architecture, detecting whether equipment operation conditions in the data to be detected are abnormal or not, classifying the equipment operation conditions if the equipment operation conditions are abnormal, and sending alarm indication information.
Compared with the prior art, the invention has the following beneficial effects:
the abnormal working condition detection algorithm can simultaneously process the acquired video data and the input variable data based on the constructed network model architecture, improves the accuracy of off-line learning and the efficiency of on-line diagnosis, and further improves the use experience of users. Meanwhile, a compressed neural network abnormal voiceprint analysis method based on deep learning is adopted, the characteristics of normal and various abnormal sounds can be automatically learned through a large number of samples of known labels, the characteristics of the sounds do not need to be analyzed manually, and the compressed neural network abnormal voiceprint analysis method is conveniently used for processing other similar problems without being modified in a large amount; adopting a recurrent neural network model capable of processing variable-length sequence input: a cyclic neural network which can accept variable-length sequences as input is used as an input layer, and a deep neural network model is constructed on the basis of the cyclic neural network, so that the problem that all the input is required to have the same scale and the length of the audio is not fixed in the traditional method for processing the audio by using a compressed neural network is successfully solved.
Drawings
Fig. 1 is a schematic view of a structural module of the present invention.
Detailed Description
The following detailed description of embodiments of the invention is provided in connection with the accompanying drawings and the examples.
As shown in fig. 1, an abnormal working condition voiceprint analysis algorithm of a device based on a compressed neural network includes a system for analyzing the voiceprint of the abnormal working condition of the device, video data of the running condition of the device and variable data;
the equipment abnormal condition voiceprint analysis system comprises:
the normal equipment voiceprint database is internally stored with voiceprint atlas data of equipment in normal operation;
the abnormal equipment voiceprint database stores voiceprint atlas data of the abnormal operation of the equipment;
the equipment audio acquisition unit acquires equipment running audio in real time by using an online sound collection tool and performs audio mapping on the acquired audio through an audio analysis tool;
the voiceprint matching unit is used for carrying out voiceprint matching on the voiceprint of the equipment operation based on the normal equipment voiceprint database, and monitoring the equipment operation state in real time:
when the operating voiceprint of the equipment is consistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal: updating the voice print of the equipment operation to a normal equipment voice print database in real time; when the operating voiceprint of the equipment is inconsistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal and sending out an early warning signal;
the video data are input into a network model architecture composed of a compressed neural network and a fully connected neural network, the variable data are directly input into the fully connected neural network to obtain the identification results of the video data and the variable data, and after deep learning is carried out on the basis of big data composed of the video data and the variable data, the parameter setting of the network model architecture is completed to obtain a target network model architecture; the method comprises the steps of obtaining video data to be detected and variable data to be detected, inputting the video data to be detected and the variable data to be detected into a target network model architecture, detecting whether equipment operation conditions in the data to be detected are abnormal or not, classifying the equipment operation conditions if the equipment operation conditions are abnormal, and sending alarm indication information.
Further, the inputting of the video data into a network model architecture composed of a compressed neural network and a fully-connected neural network, and the directly inputting of the variable data into the fully-connected neural network to obtain the recognition results of the video data and the variable data specifically include: inputting the video data into a compressed neural network, processing the video data through a convolutional layer and a pooling layer to obtain compressed data with reduced characteristic dimension, and inputting the compressed data into a full-connection layer of the full-connection neural network; obtaining variable data corresponding to the video data, adding the variable data into a full connection layer of a compressed neural network, and splicing the variable data with the compressed data to obtain target data; and carrying out operation processing on the target data through a hidden layer of the fully-connected neural network to obtain the identification results of the video data and the variable data.
The foregoing is illustrative of embodiments of the present invention and it will be further appreciated by those skilled in the art that various modifications may be made without departing from the principles of the invention and that such modifications are intended to be included within the scope of the appended claims.
Claims (2)
1. A voiceprint analysis algorithm for abnormal working conditions of equipment based on a compressed neural network is characterized in that: the method comprises an equipment abnormal working condition voiceprint analysis system, video data and variable data of equipment running conditions;
the equipment abnormal condition voiceprint analysis system comprises:
the normal equipment voiceprint database is internally stored with voiceprint atlas data of equipment in normal operation;
the abnormal equipment voiceprint database stores voiceprint atlas data of the abnormal operation of the equipment;
the equipment audio acquisition unit acquires equipment running audio in real time by using an online sound collection tool and performs audio mapping on the acquired audio through an audio analysis tool;
the voiceprint matching unit is used for carrying out voiceprint matching on the voiceprint of the equipment operation based on the normal equipment voiceprint database, and monitoring the equipment operation state in real time:
when the operating voiceprint of the equipment is consistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal: updating the voice print of the equipment operation to a normal equipment voice print database in real time; when the operating voiceprint of the equipment is inconsistent with the corresponding voiceprint in the normal equipment voiceprint database, judging that the equipment is abnormal and sending out an early warning signal;
the video data are input into a network model architecture composed of a compressed neural network and a fully connected neural network, the variable data are directly input into the fully connected neural network to obtain the identification results of the video data and the variable data, and after deep learning is carried out on the basis of big data composed of the video data and the variable data, the parameter setting of the network model architecture is completed to obtain a target network model architecture; the method comprises the steps of obtaining video data to be detected and variable data to be detected, inputting the video data to be detected and the variable data to be detected into a target network model architecture, detecting whether equipment operation conditions in the data to be detected are abnormal or not, classifying the equipment operation conditions if the equipment operation conditions are abnormal, and sending alarm indication information.
2. The compressed neural network-based equipment abnormal condition voiceprint analysis algorithm according to claim 1, wherein the voiceprint analysis algorithm comprises the following steps:
the method comprises the following steps that video data are input into a network model architecture formed by a compression neural network and a full-connection neural network, variable data are directly input into the full-connection neural network, and identification results of the video data and the variable data are obtained, and the method specifically comprises the following steps: inputting the video data into a compressed neural network, processing the video data through a convolutional layer and a pooling layer to obtain compressed data with reduced characteristic dimension, and inputting the compressed data into a full-connection layer of the full-connection neural network; obtaining variable data corresponding to the video data, adding the variable data into a full connection layer of a compressed neural network, and splicing the variable data with the compressed data to obtain target data; and carrying out operation processing on the target data through a hidden layer of the fully-connected neural network to obtain the identification results of the video data and the variable data.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110697792.2A CN113450827A (en) | 2021-06-23 | 2021-06-23 | Equipment abnormal condition voiceprint analysis algorithm based on compressed neural network |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110697792.2A CN113450827A (en) | 2021-06-23 | 2021-06-23 | Equipment abnormal condition voiceprint analysis algorithm based on compressed neural network |
Publications (1)
Publication Number | Publication Date |
---|---|
CN113450827A true CN113450827A (en) | 2021-09-28 |
Family
ID=77812265
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110697792.2A Pending CN113450827A (en) | 2021-06-23 | 2021-06-23 | Equipment abnormal condition voiceprint analysis algorithm based on compressed neural network |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113450827A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115588439A (en) * | 2022-12-13 | 2023-01-10 | 杭州兆华电子股份有限公司 | Fault detection method and device of voiceprint acquisition device based on deep learning |
CN116994609A (en) * | 2023-09-28 | 2023-11-03 | 苏州芯合半导体材料有限公司 | Data analysis method and system applied to intelligent production line |
-
2021
- 2021-06-23 CN CN202110697792.2A patent/CN113450827A/en active Pending
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115588439A (en) * | 2022-12-13 | 2023-01-10 | 杭州兆华电子股份有限公司 | Fault detection method and device of voiceprint acquisition device based on deep learning |
CN116994609A (en) * | 2023-09-28 | 2023-11-03 | 苏州芯合半导体材料有限公司 | Data analysis method and system applied to intelligent production line |
CN116994609B (en) * | 2023-09-28 | 2023-12-01 | 苏州芯合半导体材料有限公司 | Data analysis method and system applied to intelligent production line |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113870260B (en) | Welding defect real-time detection method and system based on high-frequency time sequence data | |
WO2022116570A1 (en) | Microphone array-based method for locating and identifying fault signal in industrial equipment | |
CN113450827A (en) | Equipment abnormal condition voiceprint analysis algorithm based on compressed neural network | |
CN114898466A (en) | Video motion recognition method and system for smart factory | |
CN115824519B (en) | Comprehensive diagnosis method for valve leakage faults based on multi-sensor information fusion | |
CN114495983A (en) | Equipment failure voiceprint monitoring system based on cloud edge collaboration | |
CN111678699B (en) | Early fault monitoring and diagnosing method and system for rolling bearing | |
CN114118219A (en) | Data-driven real-time abnormal detection method for health state of long-term power-on equipment | |
CN116625683A (en) | Wind turbine generator system bearing fault identification method, system and device and electronic equipment | |
CN117562311A (en) | Detection system of high-performance electronic cigarette atomizer | |
CN117114420B (en) | Image recognition-based industrial and trade safety accident risk management and control system and method | |
CN115106615B (en) | Welding deviation real-time detection method and system based on intelligent working condition identification | |
CN115931141A (en) | Temperature identification method of infrared temperature measurement map based on improved ANN algorithm | |
CN114839960A (en) | Method and system for detecting vehicle fault based on artificial intelligence algorithm | |
CN114596591A (en) | Service staff gesture standard recognition and detection method triggered by voice recognition | |
CN114233581A (en) | Intelligent patrol alarm system for fan engine room | |
CN114049598A (en) | State identification method and device of power primitive, storage medium and electronic equipment | |
CN113138894A (en) | Experimental equipment monitoring method based on power parameter monitoring and screen information identification | |
CN114764867A (en) | Fan fault diagnosis system and method based on image main feature extraction and application | |
CN115556099B (en) | Sustainable learning industrial robot fault diagnosis system and method | |
Wang et al. | Adversarial based unsupervised domain adaptation for bearing fault diagnosis | |
CN116560894B (en) | Unmanned aerial vehicle fault data analysis method, server and medium applying machine learning | |
CN117984024B (en) | Welding data management method and system based on automatic production of ship lock lambdoidal doors | |
CN117784710B (en) | Remote state monitoring system and method for numerical control machine tool | |
CN111078966B (en) | Remote fault diagnosis method and device for hydraulic machine and storage medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication |