Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification
In modern digital manufacturing, nearly 79.6% downtime of machine tool is caused by its mechanical failures. Predictive maintenance (PdM) is a useful way to minimize the machine downtime and the associated costs. One of the challenges with PdM is early fault detection in time-varying operational conditions, which means mining sensitive fault features from condition signals in long term running. However, fault features are often weakened and disturbed by the time-varying harmonics and noise during machining process. Existing analysis methods of these complex and diverse data are inefficient and time consuming. This paper proposes a novel method for early fault detection in time-varying conditions. In this study, a deep learning model is constructed to automatically select the impulse responses from the vibration signals in long term running of 288 days. Then, dynamic properties are identified from the selected impulse responses to detect the early mechanical fault under time-varying conditions. Compared to traditional methods, the experimental results in this paper have proved that our method was not affected by time-varying conditions and showed considerable potential for early fault detection in manufacturing
CNC machine tool, deep learning, dynamic identification, fault diagnosis, feature extraction.