Incipient fault detection benefited from voting fusion strategy on analysis of process variation

Actual industrial processes often have complex system dynamics and operating conditions may vary over time. Thus the constant control limits (CCLs) are loose and not suitable for monitoring the incipient faults. To address this issue, a process variation driven voting fusion strategy (PVVF) method is proposed to detect incipient faults under varying operating conditions. Because the normal control limits cannot be obtained and based on the definition of Q statistic, the proposed method uses the predicted states to generate varying control limits (VCLs) to track the actual system dynamics. Then the process information is produced by measuring the departure between the current actual states and above VCLs. The voting fusion strategy is used to combine process information of each variable to monitor process variations. When tested on a real industrial process, the proposed method is sensitive to the incipient faults under varying operating conditions.

Automated summary

The study introduces a process variation driven voting fusion strategy to detect incipient faults in complex industrial processes, utilizing varying control limits to track actual system dynamics and combining process information of each variable using a voting fusion strategy to monitor process variations. When tested on a real industrial process, the method shows sensitivity to incipient faults under varying operating conditions.