The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

中文翻译：

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特征处理在基于深度学习的电机状态监测中的重要性

深度学习（DL）的出现改变了行业中的诊断和预后技术。它在工业诊断领域取得了巨大的进步，在维护和维护工业4.0中发挥了关键作用，也为工业5.0铺平了道路。它已在工业子系统的状态监视中变得很普遍，最典型的例子是电动机。由于各种原因，各种应用中的电动机开始恶化。因此，监视它们的状况对于维持运行和保持效率至关重要。本文针对输入数据和特征处理技术，对基于DL的电动机状态监控进行了最新的综述。特别，本文就使用这些特征处理技术针对哪些问题以及如何解决这些问题的意义，回顾了各种输入功能在DL模型在电机状态监测中的有效性方面的应用。此外，它讨论并回顾了DL模型，基于DL的电动机诊断方法，混合故障诊断技术的进展，指出了这些模型的重要开放挑战，并为DL模型的未来发展指明了方向。这项审查将帮助研究人员确定与特征处理相关的研究差距，以便他们可以有效地促进应用于运动状态监测的DL模型的实现。它讨论并回顾了DL模型，基于DL的电动机诊断方法，混合故障诊断技术的进展，指出了这些模型的重要开放挑战，并为DL模型的未来发展指明了方向。这项审查将帮助研究人员确定与特征处理相关的研究差距，以便他们可以有效地促进应用于运动状态监测的DL模型的实现。它讨论并回顾了DL模型，基于DL的电动机诊断方法，混合故障诊断技术的进展，指出了这些模型的重要开放挑战，并为DL模型的未来发展指明了方向。这项审查将帮助研究人员确定与特征处理相关的研究差距，以便他们可以有效地促进应用于运动状态监测的DL模型的实现。