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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The rolling element bearing is an important component in most rotating machinery. The unexpected failure of a bearing may cause the whole mechanism to break down. Hence, research has focused on developing effective intelligent fault diagnosis to generate more accurate and robust diagnostic results. Bearing fault diagnosis based on stacked sparse autoencoder (SSAE) architecture is proposed in this study. SSAE is capable of providing a featureless methodology for bearing fault diagnosis. However, the architecture of SSAE is greatly influenced by its hyperparameter settings and there is no standard method of determining the optimal hyperparameter values. In addition, the standard learning algorithm used in SSAE architecture is time-intensive. In this paper, a method that combines differential evolution and a resilient back-propagation approach is proposed to improve the performance of SSAE networks in bearing fault classification. The differential evolution approach optimised SSAEs hyperparameters such as the hidden nodes number, weight decay parameter, sparsity parameter, and weight of the sparsity penalty term, that are associated with each hidden layer of SSAE networks. An increase in the hidden layers of SSAE will further complicate the hyperparameter selection process. The resilient back-propagation training algorithm is used to train the SSAE network due to its low computation cost. Results from analysis of three databases demonstrate that the proposed model achieved 99% performance accuracy in bearing fault diagnosis. The proposed model is found to be more user-friendly and effective in handling multi-condition of bearing faults compared to the original autoencoder.

The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures. However, under low-speed operating conditions, the diagnosis of bearing components remains a problem. In this paper, the adaptive resilient stacked sparse autoencoder (ArSSAE) is proposed to compensate for the shortcomings of conventional fault diagnosis systems at low speed. The efficiency of the proposed ArSSAE model is initially assessed using the CWRU database. Then, the proposed model is evaluated on actual vibration analysis (VA) and acoustic emission (AE) signals measured on a bearing test rig at low operating speeds (48-480 rpm). Overall, the analysis demonstrates that the ArSSAE model is able to perform an accurate diagnosis of bearing components under low-speed conditions. INDEX TERMS Low speed, bearing fault diagnosis, vibration analysis, acoustic emission analysis, adaptive resilient stacked sparse autoencoder (ArSSAE).

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