Topic
Condition monitoring
About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.
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TL;DR: The performances of three of the MEMS accelerometers from different manufacturers are investigated and compared to a well calibrated commercial accelerometer used as a reference for MEMS sensors performance evaluation.
Abstract: With increasing demands for wireless sensing nodes for assets control and condition monitoring; needs for alternatives to expensive conventional accelerometers in vibration measurements have been arisen. Micro-Electro Mechanical Systems (MEMS) accelerometer is one of the available options. The performances of three of the MEMS accelerometers from different manufacturers are investigated in this paper and compared to a well calibrated commercial accelerometer used as a reference for MEMS sensors performance evaluation. Tests were performed on a real CNC machine in a typical industrial environmental workshop and the achieved results are presented.
134 citations
TL;DR: In this article, the authors proposed an approach for continuous, online calculation of damage accumulation using standard turbine performance parameters and physics of failure methodology, which can be used to identify the root cause of critical failure modes and theoretical damage models are developed to describe the relationship between the turbine operating environment, applied loads and the rate at which damage accumulates.
Abstract: Wind turbine condition monitoring systems provide an early indication of component damage, allowing the operator to plan system repair prior to complete failure. However, the resulting cost savings are limited because of the relatively low number of failures that may be detected and the high cost of installing the required measurement equipment. A new approach is proposed for continuous, online calculation of damage accumulation using standard turbine performance parameters and Physics of Failure methodology. The wind turbine system is assessed in order to identify the root cause of critical failure modes and theoretical damage models are developed to describe the relationship between the turbine operating environment, applied loads and the rate at which damage accumulates. Accurate estimates may then be made in real time concerning the probability of failure for specific failure modes and components. The methodology is illustrated for a specific failure mode using a case study of a large wind farm where a significant number of gearbox failures occurred within a short space of time. Such an approach may be implemented at relatively low cost and offers potential for significant improvements in overall wind turbine maintenance strategy. Copyright © 2009 John Wiley & Sons, Ltd.
134 citations
TL;DR: In this article, a condition monitoring method of insulated-gate bipolar transistor (IGBT) modules is proposed to improve the reliability of power electronic systems to comply with more stringent constraints on safety, cost, and availability.
Abstract: Power electronic systems have gradually gained an important status in a wide range of industrial applications such as renewable generation, motor drives, automotive, and railway transportation. Accordingly, recent research makes an effort to improve the reliability of power electronic systems to comply with more stringent constraints on safety, cost, and availability. The power devices are one of the most reliability-critical components in power electronic systems. Therefore, its condition monitoring plays an important role to improve the reliability of power electronic systems. This paper proposes a condition monitoring method of insulated-gate bipolar transistor (IGBT) modules. In the first section of this paper, a structure of a conventional IGBT module and a related parameter for the condition monitoring are explained. Then, a proposed real-time on-state collector–emitter voltage measurement circuit and condition monitoring strategies under different operating conditions are described. Finally, experimental results confirm the feasibility and effectiveness of the proposed method.
134 citations
TL;DR: A novel model for fault diagnosis based on empirical mode decomposition (EMD) and multi-class transductive support vector machine (TSVM) is applied to diagnose the faults of the gear reducer and the experimental results obtain a very high diagnosis accuracy.
133 citations
11 May 2015
TL;DR: A systematic approach for the automated training of condition monitoring systems for complex hydraulic systems is developed and evaluated and the classification rate for random load cycles was enhanced by a distribution analysis of feature trends.
Abstract: In this paper, a systematic approach for the automated training of condition monitoring systems for complex hydraulic systems is developed and evaluated. We analyzed different fault scenarios using a test rig that allows simulating a reversible degradation of component's conditions. By analyzing the correlation of features extracted from raw sensor data and the known fault characteristics of experimental obtained data, the most significant features specific to a fault case can be identified. These feature values are transferred to a lower-dimensional discriminant space using linear discriminant analysis (LDA) which allows the classification of fault condition and grade of severity. We successfully implemented and tested the system for a fixed working cycle of the hydraulic system. Furthermore, the classification rate for random load cycles was enhanced by a distribution analysis of feature trends.
133 citations