Abstract
Predictive maintenance (PdM) is an advanced technique to predict the time to failure (TTF) of a system. PdM collects sensor data on the health of a system, processes the information using data analytics, and then establishes data-driven models that can forecast system failure. Deep neural networks are increasingly being used as these data-driven models owing to their high predictive accuracy and efficiency. However, deep neural networks are often criticized as being “black boxes,” which owing to their multi-layered and non-linear structure provide little insight into the underlying physics of the system being monitored and that are nontransparent and untraceable in their predictions. In order to address this issue, the layer-wise relevance propagation (LRP) technique is applied to analyze a long short-term memory (LSTM) recurrent neural network (RNN) model. The proposed method is demonstrated and validated for a bearing health monitoring study based on vibration data. The obtained LRP results provide insights into how the model “learns” from the input data and demonstrate the distribution of contribution/relevance to the neural network classification in the input space. In addition, comparisons are made with gradient-based sensitivity analysis to show the power of LRP in interpreting RNN models. The LRP is proved to have promising potential in interpreting deep neural network models and improving model accuracy and efficiency for PdM.
Highlights
In the era of Industry 4.0, the Industrial Internet of Things (IIoTs), smart manufacturing, and cyber physical systems [1] are providing stimulus to manufacturing sectors where smart sensors, artificial intelligence, etc. are employed to improve manufacturing performance
It is desired to examine the effectiveness of using layer-wise relevance propagation (LRP) techniques to interpret long short term memory (LSTM)-recurrent neural network (RNN) models, To do this, a model developed by Wu et al [26] based on data published by the Center for Intelligent Maintenance Systems (IMS) at the University of Cincinnati [27] was used as a case study
Unlike LRP analysis for an image recognition application, where the heat map overlaid on an image gives an intuitive explanation on the patterns learned by the model, a little bit more care must be exercised in interpreting a heat map overlaid on a time-series feature matrix in this case
Summary
In the era of Industry 4.0, the Industrial Internet of Things (IIoTs), smart manufacturing, and cyber physical systems [1] are providing stimulus to manufacturing sectors where smart sensors, artificial intelligence, etc. are employed to improve manufacturing performance. RNN-based algorithms have been widely reported as a cutting-edge technique in PdM and have achieved great prediction accuracy in various applications, they are commonly recognized as “black box” approaches, due to their multi-layered and non-linear structure that consists of hundreds of thousands of parameters that must be determined through training [14]. These properties of an RNN make it nontransparent with its predictions difficult to trace.
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