Industrial Fault Diagnosis With Incremental Learning Capability Under Varying Sensory Data
Han Zhou, Hongpeng Yin, Yan Qin, Chau Yuen
Abstract
Evolving monitoring requirements may necessitate the addition of new sensors or the exclusion of old ones. Unfortunately, traditional data-driven fault diagnosis methods usually hold the assumption that the number of sensors remains constant throughout the monitoring process, so they need to be retrained with intractable computation to account for the varying sensor behaviors. This article designs a fault diagnosis method that deals with varying sensor behaviors in an online fashion. First, we list potential sensor varying behaviors by providing definitions of sensor states and sensor state transitions. Then, this article proposes the incremental varying sensory data-driven fault diagnosis model (IVSM). IVSM is able to update in an incremental manner under varying sensory data, with a theoretical performance guarantee. The primary objective of IVSM is to continuously map the heterogeneous sensory data within different time into a unified subspace, thereby enabling the direct measurement of heterogeneous and varying sensory data. Subsequently, it constructs a fault identification classifier within this unified subspace to determine the presence of faulty conditions in the systems. Its effectiveness and efficiency are verified by experimental results obtained from two public industrial systems and one practical industrial plant.