Electrical Measurements And Measuring Instruments Rajput Pdf 33
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With the development of software and hardware, the feature extraction technique has become a mature process. There are also many software packages available, which provide a variety of feature extraction methods. The main features that are considered in the literature reviewed are often provided by the feature extraction packages as well. The reliability of the feature extraction methods is measured mostly by the performance of the classifiers, and many of the articles reviewed discussed the reliability of the feature extraction algorithms and the classifiers, and the majority of them showed that the robustness of the feature extraction is often a key factor in the fault detection accuracy. In terms of the fault diagnosis method, many articles employed the support vector machine, neural network, and fuzzy logic, which are the most popular AI techniques. The overall diagnostic performance was also evaluated in terms of accuracy and errors in some of the articles reviewed. However, none of the articles reviewed has described in detail the performance of the feature extraction and fault diagnosis method when applied to different machine systems, which is a limitation of this paper. To answer the RQ 2, some of the papers developed features and classifiers, which can be easily implemented in the online vibration monitoring system. While some other features and classifiers require more elaborate design, which is a more significant limitation of this study.
The reviewed articles discussed the different types of classifiers, the number of features used, the feature selection methods, and the AI techniques used for vibration data analysis. The proposed and existing machine diagnostics models are discussed in detail, and the conclusions are drawn. The majority of the reviewed literature is focused on the evaluation of the accuracy of the machine diagnosis in terms of the diagnostic accuracy, fault detection, fault classification, and the reliability of the measured parameters. The robustness of the diagnostic model is also discussed in detail. However, the reviewed literature did not pay much attention to the failure identification and fault isolation.
Lastly, the reliability of the data acquisition has been discussed, which includes the overview of the various instruments, the configuration of the acquisition systems, the noise sources, and the data quality issues that affect the reliability of the data acquisition. The general conclusions are drawn. This research can serve as a good review of the existing literature on machine monitoring and fault diagnosis and provide a guideline to the practitioners.
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