Skip to Main Content
Condition monitoring (CM) has been recognized as a more effective failure prevention paradigm than the time-based counterpart. CM can be performed via an array of sensors providing multiple, real-time equipment degradation information with broad coverage. However, loss of sensor readings due to sensor abnormalities and/or malfunction of connectors has long been a hurdle to reliable fault diagnosis and prognosis in multichannel CM systems. The problem becomes more challenging when the sensor channels are not synchronized because of different sampling rates used and/or time-varying operational schemes. This paper provides a nonparametric sensor recovery technique and a semi-parametric alternative to enhance the robustness of multichannel CM systems. Based on historical data, models for all the sensor signals are constructed using functional principal component analysis (FPCA), and functional regression (FR) models are developed for those correlated signals. These models with parameters updated in online implementation can be used to recover the lost sensor signals. A case study of aircraft engines is used to demonstrate the capability of the proposed approaches. In addition to recovering asynchronous sensor signals, the proposed approaches are also compared with the Elman neural network as a popular alternative in recovering synchronous sensor signals.