Abstract
Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.
Highlights
With the rapid development of science and technology in modern society, the developmental law of machinery and equipment has become considerably large scale, complex, and automated
Prognostic techniques relate to the remaining useful life (RUL) prediction, which is used in planning an effective maintenance strategy that can improve system reliability [5]
Deep neural network (DNN) is developed based on deep learning theory, which can enhance the accuracy of big data classification [33] and effectively overcome the preceding shortcomings
Summary
With the rapid development of science and technology in modern society, the developmental law of machinery and equipment has become considerably large scale, complex, and automated. Machinery condition monitoring and fault diagnosis are critical for modern industrial manufacturing. Effective condition monitoring enables the early detection of faults, with the consideration of downtime, maintenance cost, operation reliability, and production efficiency. Research on machinery condition monitoring and fault diagnosis are practically significant [1, 2]. The purposes of machinery condition monitoring and fault diagnosis are to determine the cause of abnormality and conduct necessary countermeasures by capturing the past and present condition data of equipment, such as vibration, noise, temperature, and lubrication state. A comprehensive condition monitoring program consists of three phases, namely, feature extraction, fault diagnosis, and prediction [3]. Feature extraction and fault diagnosis are usually used in detecting the abnormal state, determining the fault location, and predicting the failure extent [4]. The realization of the importance of optimal sensor placement in condition monitoring system and optimal sensor placement methods are investigated
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