A longitudinal-standardization multi-period PCA fault detection strategy based-on adaptive confidence limit

The failure rate of non-steady conditions is much higher than the failure rate of steady conditions. So, it is important to monitor non-steady conditions of system. The systems´ monitoring results indicate that there are large false alarms or missing alarms based on traditional process control methods. The primary problems are higher data dimension, more complex correlation among variables, non-Gaussian distribution, the signal mutations and so on. Hence, this paper proposed a novel Longitudinal-standardization multi-period PCA fault detection strategy based on adaptive confidence limit (LMPCA-ACL) for periodic non-steady conditions. This LMPCA-ACL strategy comprises three helpful parts as follows. First one is to transform the non-Gaussian normal data into Gaussian data through a novel longitudinal-standardization (LS). The second part is to utilize the proposed multi-period PCA algorithm to reduce dimensions, remove correlation and improve the monitoring accuracy. The third part is to build the adaptive confidence limit to resolve the problems of signal mutations and real-time monitoring by the dynamic data window method. In this paper, the LMPCA-ACL strategy is applied to real-time monitor the motor cyclical process of loading and unloading. The examination results indicate that the LMPCA-ACL strategy is superior to other methods in fault detection if the system is under periodic non-steady conditions.