VM-Based Baseline Predictive Maintenance Scheme

Most conventional FDC approaches are used to find the TDs required for monitoring and the TDs' related key parameters that need to be monitored, and then apply the SPC approach to detect the faults. However, in a practical situation, an abnormal key-parameter value may not be caused solely by its own TD; it may result from the other related parameters. Therefore, accurate fault classification or diagnosis may not be achieved. Moreover, most conventional PdM methods require a library of degradation patterns from previous run-to-failure data sets. Without those massive historical failure data, the PdM methods may not function properly. In this paper, we propose a virtual-metrology- (VM) based BPM scheme that possesses the capabilities of FDC and PdM. The BPM scheme contains the TD baseline model, FDC logic, and a RUL predictive module. The TD baseline model generated by the VM technique is applied to serve as the reference for detecting the fault. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished, the problem of the conventional SPC method mentioned above can be resolved, and the requirement of massive historical failure data can also be released.