Attention-Based LSTM Network for Rotatory Machine Remaining Useful Life Prediction

Abstract

As one of the key components in mechanical systems, rotatory machine plays a significant role in safe and stable operation. Accurate prediction of the Remaining Useful Life (RUL) of rotatory machine contributes to realization of intelligent operation and maintenance for mechanical manufacturing. In order to overcome the limitations of traditional machine learning algorithms in dealing with complex nonlinear signals, a novel prediction framework for RUL of rotatory machine based on deep learning is proposed in this paper. One-dimensional convolutional neural network is utilized to extract local features from the original signal sequence. In addition, the proposed framework analyzes sensor signals and predicts RUL by combining Long Short-Term Memory (LSTM) network with attention mechanism. Multi-layer LSTM is set up to extract useful temporal features layer by layer and improve the robustness of the model, while attention mechanism is able to effectively solve the problem of information loss in the long-distance signal transmission of LSTM. Through the feature extraction of multi-layer LSTM and the strong supervision ability of attention mechanism, the RUL of rotatory machine can be accurately predicted. The experimental results show that the proposed method for RUL estimation is efficient and has higher prediction accuracy than the traditional machine learning algorithms.

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

“…The purpose of their approach is to minimize the cost of maintenance and develop a system of predictive maintenance for optimisation predictive repair. Zhang et al [ 121 ] used vibration sensors for accurate prediction of the remaining useful life of the rotatory machines. Deep learning model combined a long short-term memory neural network with an attention mechanism for maintenance in mechanical manufacturing.…”

“…Zhang et al [121] As a key component of mechanical systems, rotatory machine has significant influence upon the whole system, and the degradation of rotatory machine may lead to deadly industrial accidents. Therefore, prognostics and health management (PHM) technology is highly desired to reduce maintenance costs and improve system reliability and safety.…”

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy

“…In addition, the nonlinear fitting ability of the SVR network is based on the use of similar history samples, and the SVR cannot reflect further abnormal fluctuations using rare similar history samples [66]. By contrast, LSTM networks have stronger feature extraction capabilities than SVR networks [67][68][69] and can use these to learn additional features pertaining to abnormal fluctuations. These features provide LSTM networks with more powerful processing capabilities for abnormal fluctuations and gives them smaller prediction errors than SVR networks.…”

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