Incipient fault detection with smoothing techniques in statistical process monitoring. (May 2017) - Ji, Hongquan; He, Xiao; Shang, Jun; Zhou, Donghua
Abstract: In modern industry, detecting incipient faults timely is of vital importance to prevent serious system performance deterioration and ensure optimal process operation. Recently, multivariate statistical process monitoring (MSPM) techniques have been extensively studied and widely applied to modern industrial systems. However, conventional fault detection indices utilized in statistical process monitoring are not sensitive to incipient faults with small magnitude. In this paper, by introducing two representative smoothing techniques, novel incipient fault detection strategies based on a generic fault detection index in MSPM are proposed. Fault detectability for each proposed strategy is analyzed. In addition, the effects of the smoothing parameters on fault detection, including advantages and disadvantages, are also investigated. Finally, case studies on a numerical example and two practical industrial processes are carried out to demonstrate the effectiveness of the proposed incipient fault detection strategies. Abstract : Highlights: Two novel strategies are proposed for incipient fault detection. Fault detectability for each proposal is analyzed, illustrating their efficiency. Corresponding geometric interpretation for the proposal is provided. Some guidelines for selecting parameters in the proposals are suggested. Simulations and practical experiments demonstrate the effectiveness of proposals.