Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers.

Abstract: Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network

Abstract: The diagnosis of the key components of rotating machinery systems is essential for the production efficiency and quality of manufacturing processes. The performance of the traditional diagnosis method depends heavily on feature extraction, which relies on the degree of individual's expertise or prior knowledge. Recently, a deep learning (DL) method is applied to automate feature extraction. However, training in the DL method requires a massive amount of sensor data, which is time consuming and poses a challenge for its applications in engineering. In this paper, a new data-driven fault diagnosis method based on compressed sensing (CS) and improved multiscale network (IMSN) is proposed to recognize and classify the faults in rotating machinery. CS is used to reduce the amount of raw data, from which the fault information is discovered. At the same time, it can be used to generate sufficient training samples for the subsequent learning. The one-dimensional compressed signal is converted to two-dimensional image for further learning. An IMSN is established for learning and obtaining deep features. It improves the diagnosis performance of the DL process. The faults of the key components are identified from a softmax model. Experimental analysis is performed to verify effectiveness of the proposed data-driven fault diagnosis method.

Assessment of Condition Monitoring Techniques for Offshore Wind Farms

Abstract: This paper discusses the results of an extensive investigation to assess the added value of various techniques of health monitoring to optimise the maintenance procedures of offshore wind farms. This investigation has been carried out within the framework of the EU funded CONMOW project (Condition Monitoring for Offshore Wind Farms) which was carried out from 2002 through 2007, [5]. A small wind farm of five turbines has been instrumented with several condition monitoring systems and also with the “traditional” measurement systems. Analyses of these measurements and of data collected by the turbine's SCADA systems have been performed to assess (1) if failures can be detected; (2) if so, if they can be detected at an early stage and their progress over time can be monitored; and (3) if criteria are available to assess the component's health. Several data analysis methods and measurement configurations have been developed, applied, and tested. This paper first describes the use condition monitoring to change from scheduled and corrective maintenance to condition based maintenance. Second, the paper describes the CONMOW project, and the major results. viz. the assessment of the usefulness and capabilities of condition monitoring systems and algorithms for identifying early failures. Finally, the economic consequences of applying condition monitoring systems have been quantified and assessed.