Abstract
In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method.
Highlights
In the production processes of modern industries, the performance of rotating mechanical equipment may degrade over time, even resulting in failure due to long-term operation under severe conditions such as high speed, high temperature, high pressure, and heavy loads
In the feature extraction stage, the original vibration signals collected by multiple sensors are sent to the convolutional neural network (CNN)-long short-term memory (LSTM) network for the extraction of spatiotemporal features, which contain operating status information; In the prediction stage, the attention-Bi-LSTM is trained to predict the trend of the features; Entropy 2022, 24, 164
In order to verify the prediction effect of the proposed attention-Bi-LSTM model, LSTM, Bi-LSTM, and attention-LSTM were applied to the Intelligent Maintenance Systems (IMS) dataset for comparison
Summary
In the production processes of modern industries, the performance of rotating mechanical equipment may degrade over time, even resulting in failure due to long-term operation under severe conditions such as high speed, high temperature, high pressure, and heavy loads. Xia Mei et al divided the monitoring data into different health stages [15] Based on this approach, the RUL of equipment was predicted using de-noising auto-encoder-based deep neural networks (DNNs). The above RUL methods can estimate how long it is until a fault will occur based on historical information, they are unable to provide the exact degradation period and fault type To solve this problem, based on gray relational analysis, Wei X U et al used a neural network model to predict the future state of a rolling bearing [16]. In this paper, CNN, LSTM, and support vector classification (SVC) are combined to establish a novel three-stage fault prediction model for rotating mechanical equipment based on vibration signals from multiple sensors.
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