Fatigue factor recessive anomaly detection and fault diagnosis method based on LSTM

A fault diagnosis and anomaly detection technology, applied in the direction of instruments, biological neural network models, character and pattern recognition, etc., can solve problems such as equipment data imbalance and high noise

Active Publication Date: 2020-06-30
天津开发区精诺瀚海数据科技有限公司
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Problems solved by technology

[0004] In order to solve the problems caused by equipment data imbalance, high noise, timing and other characteristics in the field of intel

Method used

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  • Fatigue factor recessive anomaly detection and fault diagnosis method based on LSTM
  • Fatigue factor recessive anomaly detection and fault diagnosis method based on LSTM
  • Fatigue factor recessive anomaly detection and fault diagnosis method based on LSTM

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Embodiment Construction

[0066] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0067] S1. Collect the time-series sample data of the target equipment for diagnosis. The sample contains normal data, abnormal data and the fault type of the abnormal data. Use the EMD method to perform empirical mode decomposition on the time-series sample data of the equipment;

[0068] The experimental verification of the present invention uses experimental data including three types of faults: inner raceway fault, outer raceway fault and ball fault;

[0069] S2. Extract the normal data samples in the time series sample data, and use the deep neural network based on LSTM to construct the equipment vibration signal prediction model;

[0070] The process flow of the LSTM-based vibration signal prediction model proposed by the present invention is described as follows, and the model flow chart is shown in Figure 1.

[0071] Step 1: ...

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Abstract

The invention discloses a fatigue factor recessive anomaly detection and fault diagnosis method based on LSTM, and the method comprises the following steps: S1, collecting the time sequence data of target equipment of a related diagnosis object, and carrying out the empirical mode decomposition of the data; S2, constructing an equipment vibration signal prediction model based on LSTM by using thenormal data; S3, classifying the collected abnormal data, and constructing a fault time series data classification model based on LSTM; S4, taking the obtained mean square error MSE of the LSTM-basedvibration signal prediction model as an initial fatigue factor threshold; S5, predicting the equipment production data by using an LSTM-based vibration signal prediction model, calculating a mean square error between a predicted value and an actual value, and comparing the mean square error with an initial fatigue factor threshold to detect an abnormal signal; S6, classifying the abnormal signalsthrough a fault time series data classification model to obtain a fault diagnosis result.

Description

technical field [0001] The invention relates to the field of equipment fault diagnosis, in particular to an LSTM-based hidden abnormality detection and fault diagnosis method for fatigue factors. Background technique [0002] With the "nuclear fusion" explosion of Internet of Things, 5G, artificial intelligence, cloud computing and other technologies, the "re-industrialization" strategies formulated by major industrial countries around smart manufacturing are also rampant. In the 2019 government work report, China put forward the concept of "smart +" for the first time, and identified smart manufacturing as an important development direction of new kinetic energy for national economic development. [0003] The current fault diagnosis methods are mainly classified into two categories: methods based on signal processing and methods based on machine learning. Common methods based on signal processing include Spectral Kurtosis, Sparse Decomposition Analysis, Time-frequency Anal...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06N3/044G06F2218/08G06F2218/12G06F18/214
Inventor 冯海领焦正杉孙敬哲王汉奇王向敏赵宜斌
Owner 天津开发区精诺瀚海数据科技有限公司
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