The article delves into the development of a Non-Gaussian Process Monitoring Strategy for a Copper Cathode Manufacturing Unit (CCMU). The monitoring strategy being devised highlighted the issue of multi-stage process monitoring via the usage of Multi-block Independent Component Analysis (MBICA) techniques. MBICA is the multi-block variant of ICA technique which is prevalently used for process laden with non-Gaussian or non-normal data. Development of the monitoring strategy involved detection of fault(s) and their subsequent diagnosis. Detection of fault(s) was carried out by employment of I2 control chart whose control limit was established via Bootstrap procedure. The diagnosis of the detected fault was carried out by employment of fault diagnostic statistic. An amalgamation of MBICA and Multivariate Exponentially Weighted Moving Average (MEWMA) are also known as MBICA-MEWMA approach was also proposed for detection of incipient fault(s). The monitoring strategy thus developed was showcased for a CCMU which specialised in the manufacture of copper cathode which has got varied practical applications. The monitoring strategy thus devised was able to detect and diagnose the faults with appreciable accuracy.
The article proposes the development of a layered process monitoring strategy based on Multi- Block Kernel Principal Component Analysis (MBKPCA). MBKPCA aids in the development of a distributed process monitoring strategy by taking into account the nonlinear relationships existing amongst the measured characteristics. A distributed process monitoring strategy stratifies the proposed process into a multi-layered structure comprising of blocks, sub-blocks etc. In this article an MBKPCA based monitoring strategy was devised for a Wire Rod Manufacturing Facility (WRMF) of an Integrated Steel Plant (ISP). The proposed monitoring strategy stratified the entire process into 3 layers, with the first layer comprising the manufacturing stages, the next layer comprising the sub-stages and the third layer comprising the characteristics to be monitored within the respective sub-stages. The detection of the fault was carried out with the aid of Kernel Principal Component Analysis (KPCA) score based Hotelling T2 chart. Fault detection was followed by Fault Diagnosis, for which new Fault Diagnostic Statistics were proposed which took into account the contribution of the main and the auxiliary characteristics. The study also proposed the concept of Cumulative Percent Contribution Ratio (CPCR) to limit the number of parameters (stages/sub-stages/characteristics) that needs to be retained in fault diagnosis.
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