Kernelized relative entropy for direct fault detection in industrial rotary kilns

Abstract

The objective of this work is to use a 1-dimensional signal that reflects the dissimilarity between multidimensional probability densities for detection. With the modified Kullback-Leibler divergence, faults can be directly detected without any normality assumption or joint monitoring of related test statistics in different subspaces such as the T 2 and SPE in principal component analysis-based methods. To relieve the difficulty associated with asymptotic high-dimensional density estimates, we have estimated the density ratio rather than the densities themselves. This can be done by approximating the density ratio with kernel basis functions and learn the weights from the available data. The developed algorithm is generic and can be applied to any industrial system as long as process historical data is available. As a case study, we apply this algorithm to a real rotary kiln in operation, which is an integral part of the cement manufacturing plant of Ain El Kebira, Algeria.

Device Health Status Assessment Under the Influence of Multiple Exception Modes

“…In recent years, the fault diagnosis and Health assessment methods of machinery equipment have emerged one after another, which has a positive impact on the construction machinery. Scholars have used deep reinforcement learning (Zhong et al, 2023), machine learning algorithms (Bhat et al, 2023), principal component analysis (Bencheikh et al, 2020), wavelet packet decomposition (Hamadouche et al, 2018) and other methods to realize the fault diagnosis of cement rotary kiln. The diversity of PHM algorithms and the complexity of design factors make it challenging to choose an appropriate algorithm for a specific application (Zou et al, 2023).…”