Summary Energy harvesting technologies are growing rapidly in recent years because of limitation by energy storage and wired power supply. Vibration energy is abundant in the atmosphere and has the potential to be harvested by different mechanisms, mainly through piezoelectric and electromagnetic means. Various architectural structures were also designed for several operating conditions, namely, resonance frequency and range thereof, acceleration, and energy extraction from several motions. The advantages and disadvantages were elaborated on, and improvements on ideas from current research were discussed in this review.

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed. INDEX TERMSDeep learning, fault detection and diagnosis, current challenges, future developments. I. INTRODUCTION Safety and reliability are key factors in industrial operations. Rotating machinery is a vital component in many industries, and it is prone to failure due to harsh working conditions and long operational times [1], [2]. Examples of rotating machinery components including gears [3], pumps [4], bearings [5], shafts [6], blades [7], motors [8] and engines [9]. Failures in rotating machinery should be detected as early as possible to prevent critical damage [10] and sudden halt of machine operation. Failures may cause delays in operations and, consequently, tremendous economic loss [11]. For example, petrochemical industries lose around 20 billion dollars per year due to faults in their machine components [12]. According to a report by Duan et al. maintenance accounts for more than 60% of the total cost of aircraft engine components [13]. In the worst case, a machinery component failure may lead to loss of human life. Elasha et al. discussed a caseThe associate editor coordinating the review of this article and approving it for publication was Kezhi Li.

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A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.

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