A Review on Data-Driven Condition Monitoring of Industrial Equipment
Ruosen Qi, Jie Zhang, Katy Spencer
Abstract
This paper presents an up-to-date review of data-driven condition monitoring of industrial equipment with the focus on three commonly used equipment: motors, pumps, and bearings. Firstly, the general framework of data-driven condition monitoring is discussed and the utilized mathematical and statistical approaches are introduced. The utilized techniques in recent literature are discussed. Then, fault detection, diagnosis, and prognosis on the three types of equipment are highlighted using a variety of popular shallow and deep learning models. Applications of these techniques in recent literature are summarized. Finally, some potential future challenges and research directions are presented.
Topics & Concepts
Computer scienceVariety (cybernetics)Condition monitoringFocus (optics)Industrial equipmentFault (geology)Data scienceSystems engineeringRisk analysis (engineering)Industrial engineeringArtificial intelligenceEngineeringElectrical engineeringSeismologyOpticsPhysicsGeologyMedicineMachine Fault Diagnosis TechniquesEngineering Diagnostics and ReliabilityFault Detection and Control Systems