Nonparametric and Semi-Parametric Sensor Recovery in Multichannel Condition Monitoring Systems

作者：Liao Haitao^*; Sun Jian

来源：IEEE Transactions on Automation Science and Engineering, 2011, 8(4): 744-753.

DOI：10.1109/TASE.2011.2159265

• Asynchronous data
• condition monitoring (CM)
• functional principal component analysis (FPCA)
• sensor recovery

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.
Note to Practitioners-This paper is motivated by a couple of technical barriers in multichannel CM systems, where sensors providing multiple, real-time CM information are subject to failures and limited and/or asynchronous data transmission is unavoidable. These make reliable fault diagnosis and prognosis quite challenging. Therefore, a new sensor recovery method capable of handling sparse and/or asynchronous CM data becomes a value-added means to improve the robustness of a CM system. This paper provides a nonparametric sensor recovery approach and a semi-parametric alternative based on FPCA to overcome the challenges. These approaches are more capable than widely used alternatives, such as neural networks, in dealing with such complex CM environments.

• 出版日期2011-10