Vibration-based Data-driven Fault Diagnosis of Rotating Machines Operating Under Varying Working Conditions

The intelligent fault diagnosis of rotating machines has been significantly advanced by learning-based techniques in recent years. However, the performance of these techniques can drastically decrease under varying working conditions (VWC). This paper investigates the root causes of these decreased capabilities by analyzing the impact of VWC on each of the key steps in intelligent fault diagnosis for rotating machines. In addition, techniques proposed in the literature to mitigate these effects are reviewed and assessed for their relevance in industrial applications. A bibliometric study is also conducted to understand the evolution of research in this field over the past two decades. Beyond providing a synthesis of the existing literature, this review is intended for researchers, engineers, and industry professionals seeking to implement robust fault diagnosis systems under varying operational conditions. It offers insights on when and how these techniques can be effectively applied, depending on specific industrial scenarios and assumptions.