Untimely machinery breakdown will incur significant losses, especially to the manufacturing company as it affects the production rates. During operation, machines generate vibrations and there are unwanted vibrations that will disrupt the machine system, which results in faults such as imbalance, wear, and misalignment. Thus, vibration analysis has become an effective method to monitor the health and performance of the machine. The vibration signatures of the machines contain important information regarding the machine condition such as the source of failure and its severity. Operators are also provided with an early warning for scheduled maintenance. Numerous approaches for analyzing the vibration data of machinery have been proposed over the years, and each approach has its characteristics, advantages, and disadvantages. This manuscript presents a systematic review of up-to-date vibration analysis for machine monitoring and diagnosis. It involves data acquisition (instrument applied such as analyzer and sensors), feature extraction, and fault recognition techniques using artificial intelligence (AI). Several research questions (RQs) are aimed to be answered in this manuscript. A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. The presence of various sensors and communication devices in the emerging smart machines will present a new and huge challenge in vibration monitoring and diagnosing.

中文翻译：

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用于机器监测和诊断的振动分析：系统回顾

不合时宜的机器故障将导致重大损失，尤其是对制造公司而言，因为它会影响生产率。在运行过程中，机器会产生振动，并且存在会破坏机器系统的不想要的振动，从而导致不平衡、磨损和不对中等故障。因此，振动分析已成为监测机器健康和性能的有效方法。机器的振动特征包含有关机器状况的重要信息，例如故障来源及其严重程度。还为操作员提供了定期维护的预警。多年来，已经提出了多种分析机械振动数据的方法，每种方法都有其特点、优点和缺点。这份手稿对用于机器监测和诊断的最新振动分析进行了系统回顾。它涉及数据采集（应用的仪器，如分析仪和传感器）、特征提取和使用人工智能 (AI) 的故障识别技术。本手稿旨在回答几个研究问题 (RQ)。时域统计特征和深度学习方法的结合有望在未来得到广泛应用，可以从原始振动信号中自动提取故障特征。新兴智能机器中各种传感器和通信设备的存在，将对振动监测和诊断提出新的巨大挑战。它涉及数据采集（应用的仪器，如分析仪和传感器）、特征提取和使用人工智能 (AI) 的故障识别技术。本手稿旨在回答几个研究问题 (RQ)。时域统计特征和深度学习方法的结合有望在未来得到广泛应用，可以从原始振动信号中自动提取故障特征。新兴智能机器中各种传感器和通信设备的存在，将对振动监测和诊断提出新的巨大挑战。它涉及数据采集（应用的仪器，如分析仪和传感器）、特征提取和使用人工智能 (AI) 的故障识别技术。本手稿旨在回答几个研究问题 (RQ)。时域统计特征和深度学习方法的结合有望在未来得到广泛应用，可以从原始振动信号中自动提取故障特征。新兴智能机器中各种传感器和通信设备的存在，将对振动监测和诊断提出新的巨大挑战。时域统计特征和深度学习方法的结合有望在未来得到广泛应用，可以从原始振动信号中自动提取故障特征。新兴智能机器中各种传感器和通信设备的存在，将对振动监测和诊断提出新的巨大挑战。时域统计特征和深度学习方法的结合有望在未来得到广泛应用，可以从原始振动信号中自动提取故障特征。新兴智能机器中各种传感器和通信设备的存在，将对振动监测和诊断提出新的巨大挑战。