Abstract
Bearings are widely used in many steam turbine generator sets and other large rotating equipment. With the rapid development of contemporary industry, there is a great number of rotating equipment in various large factories, such as nuclear power plants. As the core component of rotating machinery, the failure of rolling bearings may lead to serious accidents during the industrial production operation. In order to accurately diagnose the fault status of rolling bearings, a novel long short-term memory (LSTM) model with discrete wavelet transformation (DWT) for multi-sensor fault diagnosis is proposed in this paper. The main purpose of this paper is to use the DWT-LSTM model to diagnose the health of rolling bearings. Firstly, the DWT is used to obtain detailed fault information in both different frequency and time scales. Then, the LSTM network is employed to characterize the long-term dependencies hidden in the time series of the fault information. The proposed DWT-LSTM method makes full use of the advantages of feature extraction based on expert experience and deep network learning to discover complex patterns from a large amount of data. Finally, the feasibility and efficiency of the proposed method are illustrated by comparison with the existing methods.
Highlights
discrete wavelet transformation (DWT)-long short-term memory (LSTM) model is proposed to identify the faults of rolling bearings across different types and levels of severity
The proposed method is composed of two parts: (1) the DWT method, which enriches the details of raw fault vibration signal through expert experience and knowledge; (2) LSTM neural network, which solves the long-term dependence of time-series data
The multi-scale data of multiple sensors are fused to enrich the fault features to improve the accuracy of the DWT-LSTM model
Summary
Many factories, such as nuclear power plants, have already used large rotating equipment for industrial production [1]. Rolling bearings are widely used as main components in the rotating machinery and equipment of these factories. The health status of rolling bearings is directly related to the safety of equipment operation [2]. At least 40% of rotating machinery failures are caused by the damage of rolling bearings [3]. The inability of rolling bearings will lead to a severe failure of rotating machinery, which may endanger personal safety and causes property damage. It is of great practical significance to diagnose the rolling bearing status effectively, promptly, and accurately
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