Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels

Many manufacturing systems are instrumented with image-sensing systems to monitor process performance and product quality. The low cost and rich information of the image-based sensing systems have led to high-dimensional data streams that provide distinctive opportunities for performance improvement. Among these, accurate process monitoring and fault classification are among the benefits gained from the rich information these image sensors can provide. In literature, process monitoring often refers to the step of detecting and isolating abnormal samples in a certain process. Normally, after process monitoring, fault classification is performed, and the isolated fault is classified into one or more known types of fault. Fault classification is an essential step within the process monitoring loop, at which point the type of detected and identified faults are determined (Chiang et al., 2001). Accurate fault classification can provide engineers with favorable information to isolate and diagnose system faults and anomalies to improve quality and maximize system efficiency. However, fault classification in manufacturing systems typically assumes a fixed set of fault modes. In this case, the existing fault classification model may make overconfident decisions or fail silently and, at certain times, dangerously for new unseen fault types.