CN112435686A - Power equipment fault voice recognition method based on data enhancement - Google Patents
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Abstract
The invention provides a data enhancement-based power equipment fault voice recognition method, which comprises the following steps of: firstly, collecting audio samples of common power equipment faults and marking the audio samples; then, performing framing and windowing on the audio sample through preprocessing operation; then extracting a Mel cepstrum coefficient from the preprocessed audio sample as a feature vector; then, performing data enhancement on the extracted feature vector by using a mix up technology to construct a new feature vector; and finally, inputting the enhanced training set into a ResNet network for judgment, and identifying fault sounds of different power equipment. The electric power equipment fault voice recognition method based on data enhancement can effectively solve the problem of insufficient training data in an actual electric power equipment fault voice recognition system, and improves the generalization capability of the model. When the fault voice of the power equipment is identified, the method can obtain better identification effect.
Description
Technical Field
The invention relates to the technical field of signal processing and deep learning, in particular to a power equipment fault voice identification method based on data enhancement.
Background
The power equipment plays an important role in each production link of power generation, power transmission, power distribution and the like of a power system, and any one key power equipment in the power system fails to cause operation instability and even production interruption, so that huge economic loss is caused, catastrophic consequences are brought, and life and personal safety of people are threatened. Therefore, the development of a practical power equipment fault detection system is very important, the significance is that the limitation of preventive maintenance is broken, the risk analysis capability is improved, and a user of the detection system can see the detection parameter information of the equipment, the fault result obtained by parameter processing and analysis and the early warning report at any time. Maintenance personnel can timely obtain equipment fault early warning information to check and solve problems, reliability of power equipment is enhanced, and unplanned downtime of key equipment is greatly reduced.
In addition to signals that are traditionally and widely used for detection, as the capability of sound collection and processing technology improves, the concept of sound for detection of electric power equipment is also proposed by some scholars. The sound is the radiation energy of mechanical waves of the electrical equipment from vibration to a sound transmission medium, and the sound signal contains a large amount of vibration information and is an important index for analyzing the running state of the equipment. When the equipment normally runs, the sound emitted by the equipment is different corresponding to different states of mutual movement of the machine body, the firmware, the parts and the parts, and the sound generated by the power equipment is changed when the running state is changed. At present, the audio data amount of power equipment faults is increased, but audio samples with accurate labels are few, and a more diversified sample database needs to be constructed on limited samples with labels, so that the generalization capability of the classifier is improved. In order to solve the problem that the number of labeled power equipment fault sound samples is insufficient, a data enhancement method needs to be adopted, the diversity of training samples is enriched, and therefore the generalization capability of the model is improved. When the fault voice of the power equipment is identified, the method can obtain better identification effect.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for recognizing the fault sound of the power equipment based on data enhancement.
The invention is realized by the following technical scheme.
A power equipment fault voice recognition method based on data enhancement comprises the following steps:
collecting audio samples of common power equipment faults by using a sound sensor, marking, and then dividing a training set and a testing set according to a proportion;
respectively carrying out frame division and windowing preprocessing operations on the training set and the test set audio samples;
extracting a characteristic vector from the preprocessed audio sample;
performing data enhancement on the extracted feature vector to construct a new feature vector;
and inputting the enhanced feature vector set into a ResNet network for judgment, and identifying fault sounds of different power equipment.
Further, the collecting common power equipment fault audio samples by using the sound sensor, labeling, and then dividing the training set and the testing set according to the proportion includes:
collecting audio samples of common power equipment faults by using a sound sensor: under different fault conditions of the power equipment, recording audio samples of corresponding fault conditions, wherein the sampling rate is 44100Hz, and the frequency range is 20-20000 Hz.
Dividing a training set and a testing set according to a proportion: and (4) marking the marked audio samples according to the following steps: the scale of 1 is randomly divided into a training set and a test set.
Further, the pre-processing operations of framing and windowing the training set and the test set audio samples respectively include:
framing: the audio samples are sliced into 25ms segments with a frame shift of 10 ms.
Windowing: windowing is performed on each frame of audio signal using a hamming window.
Further, the extracting the feature vector from the preprocessed audio sample includes:
and extracting 20-dimensional Mel cepstrum coefficients from each preprocessed frame of audio signal to serve as a feature vector of one frame, and averaging the 20-dimensional Mel cepstrum coefficients of the adjacent 10 frames of audio signals to form a new feature vector.
Further, the data enhancement of the extracted feature vector and the construction of a new feature vector include:
and performing data enhancement on the extracted feature vector by using a mix up technology to construct a new feature vector.
Further, the inputting the enhanced feature vector set into a ResNet network for decision, and identifying fault sounds of different power devices includes:
a ResNet network architecture is built, an activation function of network training adopts a tanh function, and the training process of the network is to obtain the minimum value through a loss function combining back propagation and gradient descent. And after training and debugging the network structure parameters to be optimal, repeating the steps of preprocessing and feature extraction on the test set sample, inputting the feature vector into a ResNet network to obtain the corresponding probability of each fault sound, and taking the fault with the highest probability as the recognition result of the audio sample.
Due to the adoption of the technical scheme, the invention has at least one of the following beneficial effects:
the ResNet network is applied to the electric power equipment fault sound recognition, an acoustic-based electric power equipment fault diagnosis model can be effectively established, and the recognition effect is better compared with the traditional support vector machine, random forest and other shallow classifiers.
The invention enhances the data of the available limited fault audio samples, enriches the diversity of the samples and improves the generalization capability of the classifier. In addition, the data enhancement also mines potential features among fault audio data, and the recognition effect is more excellent.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for recognizing a fault sound of an electrical device based on data enhancement according to the present invention.
Detailed Description
The following examples illustrate the invention in detail: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Examples
A method for recognizing a fault sound of power equipment based on data enhancement can effectively solve the problem of insufficient training data in an actual power equipment fault sound recognition system and improve the generalization capability of a model. When the fault voice of the power equipment is identified, the method can obtain better identification effect.
As shown in fig. 1, the method comprises the steps of:
step 1, collecting audio samples of common power equipment faults by using a sound sensor, marking the audio samples, and then dividing a training set and a testing set according to a proportion.
In this embodiment, the step specifically includes the following steps:
step 1.1, recording audio samples of corresponding fault conditions through a non-contact sound acquisition system under different fault conditions of the power equipment, wherein the sampling rate is 44100Hz, and the frequency range is 20-20000 Hz.
Step 1.2, dividing a training set and a testing set according to a proportion: and (3) marking different fault audio samples by adopting numbers of 0-9, and randomly dividing the marked samples into a training set and a testing set according to the ratio of 4: 1.
Step 2, respectively carrying out frame division and windowing pretreatment operations on the audio samples of the training set and the test set;
in this embodiment, the step specifically includes the following steps:
step 2.1, framing: a plurality of sampling points are grouped into an observation unit, which is called a sub-frame. The covered time is about as. An overlap region is provided between two adjacent frames, the overlap region includes a plurality of sampling points, and the covered time is about bs.
In the embodiment, the number of sampling points is 400; the covered time is 0.025 s; the overlap region contains 160 sampling points covering 0.01 s.
And 2.2, windowing each frame of audio signal by using a Hamming window. The Hamming window function is expressed as:
in the formula, N represents the frame length, alpha is a window function parameter and is generally 0.46;
step 3, extracting feature vectors from the preprocessed audio samples, and specifically comprising the following steps:
and 3.1, performing fast Fourier transform on each frame signal subjected to framing and windowing to obtain a frequency spectrum of each frame, and performing modular squaring on the frequency spectrum of the sound signal (sound sample) to obtain a power spectrum of the sound signal.
Setting sound signal Xa(k) The DFT of (1) is:
where k denotes the kth frequency of the fourier transform, x (N) denotes an input speech signal, and N denotes the number of points of the fourier transform.
Step 3.2, passing the energy spectrum through a group of Mel-scale triangular filter banks, wherein the frequency response H of the triangular filter banksm(k) Comprises the following steps:
wherein f (-) represents the center frequency,
m is the number of filters.
Step 3.3, calculating the logarithmic energy s (m) output by each filter bank, wherein the form is as follows:
step 3.4, obtaining the MFCC coefficient C (n) through Discrete Cosine Transform (DCT) in the form of:
step 3.5, averaging 20-dimensional Mel cepstrum coefficients of adjacent 10 frames of signals to form a new feature vector:
in the formula xiA 20-dimensional mel-frequency cepstral coefficient feature vector representing the ith frame, i 1, 2.
And 4, performing data enhancement on the extracted feature vector to construct a new feature vector, and specifically comprising the following steps of:
and performing data enhancement on the extracted feature vector by using a mix up technology to construct a new feature vector:
y＝λyi+(1-λyj) (7)
in the formula (x)i，yi) And (x)j，yj) Are two samples, x, randomly drawn from the training dataiAnd xjFeature vector, y, representing a sampleiAnd yjIs the label corresponding to the sample.
Representing the new training sample that is generated,
the feature vector representing the new sample, y representing the corresponding new label; and lambda belongs to [0.1 ∈ ]]。
Step 5, inputting the enhanced feature vector set into a ResNet network for judgment, and identifying fault sounds of different power equipment, specifically comprising the following steps:
step 5.1, building a ResNet network architecture, wherein an activation function of network training adopts a Relu function:
the training process of the network is to obtain the minimum value through combining the back propagation and the gradient descent and is a loss function. Specific training methods references: he K, Zhang X, Ren S, et al. deep reactive Learning for Image Recognition [ J ]. 2015.
And 5.2, after training and debugging the network structure parameters to be optimal, repeating the steps of preprocessing and feature extraction on the test set sample, inputting the feature vector into the network to obtain the corresponding probability of each fault sound, and taking the fault with the highest probability as the recognition result of the audio sample so as to judge whether the equipment has faults and give the fault type.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (6)
1. A method for recognizing fault sounds of power equipment based on data enhancement is characterized by comprising the following steps:
the method comprises the following steps of collecting audio samples of common power equipment faults by using a sound sensor, marking the audio samples, and then according to n: 1, dividing a training set and a test set in proportion;
respectively carrying out frame division and windowing preprocessing operations on the training set and the test set audio samples;
extracting a characteristic vector from the preprocessed audio sample;
performing data enhancement on the extracted feature vector to construct a new feature vector;
and inputting the new feature vector set into a ResNet network for judgment, and identifying fault sounds of different power equipment.
2. The method for recognizing the fault sound of the power equipment based on the data enhancement is characterized in that a sound sensor is used for collecting audio samples of common fault sound of the power equipment, the audio samples are labeled, and then a training set and a testing set are divided according to the proportion, and the method comprises the following steps of:
collecting audio samples of common power equipment faults by using a sound sensor: under different fault conditions of the power equipment, recording audio samples of corresponding fault conditions, wherein the sampling rate is 44100Hz, and the frequency range is 20-20000 Hz.
Dividing a training set and a testing set according to a proportion: and (4) marking the marked audio samples according to the following steps: the scale of 1 is randomly divided into a training set and a test set.
3. The method for voice recognition of power equipment failure based on data enhancement as claimed in claim 1, wherein the pre-processing operations of framing and windowing the training set and test set audio samples respectively comprise:
framing: the audio samples are sliced into 25ms segments with a frame shift of 10 ms.
Windowing: windowing each frame of audio signal with a hamming window.
4. The method for recognizing the fault sound of the power equipment based on the data enhancement is characterized in that the step of extracting the feature vector from the preprocessed audio sample comprises the following steps:
and extracting 20-dimensional Mel cepstrum coefficients from each preprocessed frame of audio signal to serve as a feature vector of one frame, averaging the 20-dimensional Mel cepstrum coefficients of adjacent N frames of audio signals to form a new feature vector, wherein N is generally 5-15.
5. The method for recognizing the fault voice of the power equipment based on the data enhancement is characterized in that the data enhancement of the extracted feature vector to construct a new feature vector comprises the following steps:
and performing data enhancement on the extracted feature vector by using a mix up technology to construct a new feature vector.
6. The method for recognizing the fault sound of the electric power equipment based on the data enhancement as claimed in claim 1, wherein the step of inputting the enhanced feature vector set into a ResNet network for decision to recognize the fault sound of different electric power equipment comprises the following steps:
a ResNet network architecture is built, an activation function of network training adopts a tanh function, and the training process of the network is to obtain the minimum value through a loss function combining back propagation and gradient descent. And after training and debugging the network structure parameters to be optimal, repeating the steps of preprocessing and feature extraction on the test set sample, inputting the feature vector into a ResNet network to obtain the corresponding probability of each fault sound, and taking the fault with the highest probability as the recognition result of the audio sample.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033490A (en) * | 2021-04-23 | 2021-06-25 | 山东省计算中心（国家超级计算济南中心） | Industrial equipment general fault detection method and system based on sound signals |
| CN113314144A (en) * | 2021-05-19 | 2021-08-27 | 中国南方电网有限责任公司超高压输电公司广州局 | Voice recognition and power equipment fault early warning method, system, terminal and medium |
| CN113327629A (en) * | 2021-05-06 | 2021-08-31 | 上海交通大学 | Power equipment sound diagnosis method and system |
| CN113551765A (en) * | 2021-08-17 | 2021-10-26 | 中冶北方（大连）工程技术有限公司 | Sound spectrum analysis and diagnosis method for equipment fault |
| CN113553465A (en) * | 2021-06-15 | 2021-10-26 | 深圳供电局有限公司 | Sound data storage method and device, computer equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109599126A (en) * | 2018-12-29 | 2019-04-09 | 广州丰石科技有限公司 | A kind of sound fault recognition method based on mel energy spectrum and convolutional neural networks |
| CN109978034A (en) * | 2019-03-18 | 2019-07-05 | 华南理工大学 | A kind of sound scenery identification method based on data enhancing |
| CN110808068A (en) * | 2019-11-11 | 2020-02-18 | 北京软通智城科技有限公司 | Sound detection method, device, equipment and storage medium |
| CN110930995A (en) * | 2019-11-26 | 2020-03-27 | 中国南方电网有限责任公司 | Voice recognition model applied to power industry |
| CN110940539A (en) * | 2019-12-03 | 2020-03-31 | 桂林理工大学 | Machine equipment fault diagnosis method based on artificial experience and voice recognition |
-
2020
- 2020-11-19 CN CN202011304659.8A patent/CN112435686A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109599126A (en) * | 2018-12-29 | 2019-04-09 | 广州丰石科技有限公司 | A kind of sound fault recognition method based on mel energy spectrum and convolutional neural networks |
| CN109978034A (en) * | 2019-03-18 | 2019-07-05 | 华南理工大学 | A kind of sound scenery identification method based on data enhancing |
| CN110808068A (en) * | 2019-11-11 | 2020-02-18 | 北京软通智城科技有限公司 | Sound detection method, device, equipment and storage medium |
| CN110930995A (en) * | 2019-11-26 | 2020-03-27 | 中国南方电网有限责任公司 | Voice recognition model applied to power industry |
| CN110940539A (en) * | 2019-12-03 | 2020-03-31 | 桂林理工大学 | Machine equipment fault diagnosis method based on artificial experience and voice recognition |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033490A (en) * | 2021-04-23 | 2021-06-25 | 山东省计算中心（国家超级计算济南中心） | Industrial equipment general fault detection method and system based on sound signals |
| CN113033490B (en) * | 2021-04-23 | 2023-09-19 | 山东省计算中心（国家超级计算济南中心） | Industrial equipment general fault detection method and system based on sound signals |
| CN113327629A (en) * | 2021-05-06 | 2021-08-31 | 上海交通大学 | Power equipment sound diagnosis method and system |
| CN113314144A (en) * | 2021-05-19 | 2021-08-27 | 中国南方电网有限责任公司超高压输电公司广州局 | Voice recognition and power equipment fault early warning method, system, terminal and medium |
| CN113553465A (en) * | 2021-06-15 | 2021-10-26 | 深圳供电局有限公司 | Sound data storage method and device, computer equipment and storage medium |
| CN113553465B (en) * | 2021-06-15 | 2023-12-19 | 深圳供电局有限公司 | Sound data warehousing method, device, computer equipment and storage medium |
| CN113551765A (en) * | 2021-08-17 | 2021-10-26 | 中冶北方（大连）工程技术有限公司 | Sound spectrum analysis and diagnosis method for equipment fault |
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Application publication date: 20210302 |