The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-vary...The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-varying data processing and quality-relevant fault detecting.How-ever,it encounters heavy computational load in model updating,and the static control limits often lead to the low fault detection rate(FDR)or high false alarm rate(FAR).In this article,we first introduce the recursive MPLS(RMPLS)method for quality-relevant fault detection and computational complexity reducing,and then combine the local information increment(LII)method to obtain the time-varying control limits.First,the proposed LII-RMPLS method is capa-ble of quality-relevant faults detection.Second,the adaptive threshold leads to higher FDRs and lower FARs compared with traditional methods.Third,the adaptive parameter-matrices-based model updating approach ensures that the proposed method has better robustness and lower computational complexity when dealing with time-varying factors.展开更多
基金gratefully acknowledge that this work is supported in part by National Natural Science Foundation of China[grant numbers 61903375 and 61673387]in part by theNatural Science Foundation of Shaanxi Province[grant number 2020JM-3].
文摘The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-varying data processing and quality-relevant fault detecting.How-ever,it encounters heavy computational load in model updating,and the static control limits often lead to the low fault detection rate(FDR)or high false alarm rate(FAR).In this article,we first introduce the recursive MPLS(RMPLS)method for quality-relevant fault detection and computational complexity reducing,and then combine the local information increment(LII)method to obtain the time-varying control limits.First,the proposed LII-RMPLS method is capa-ble of quality-relevant faults detection.Second,the adaptive threshold leads to higher FDRs and lower FARs compared with traditional methods.Third,the adaptive parameter-matrices-based model updating approach ensures that the proposed method has better robustness and lower computational complexity when dealing with time-varying factors.
