# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 1 Introduction  
Most modern equipment systems, including motors, pumps, compressors, fans, and automobile transmissions, are comprised of rotating machinery [1]. Sudden faults (e.g., breakdown or malfunction) of that rotating machinery, including rolling bearings, often cause unnecessary interruption of processes, and this eventually degrades productivity or even results in catastrophic human accidents [2]. To counteract these problems in various industrial fields, an appropriate preventive maintenance (PM) strategy for the rotating machinery should be established. The PM strategy attempts to prevent the unexpected faults of rotating machinery by detecting machinery fault symptoms in advanceof fault occurrence [3]. The PM strategy can be categorized as time-based maintenance (TBM) and condition-based maintenance (CBM) [4]. TBM is a maintenance strategy to periodically inspect or repair the equipment, whereas in CBM, the inspection or repair is performed when the symptoms of equipment faults are detected. Compared to CBM, TBM may be a more inefficient maintenance strategy because it often entails excessively frequent interruption of machinery operations for unnecessary inspection [5]. Hence, in this study, we mainly concentrate on how to more effectively execute CBM. To execute the CBM for the rotating equipment, firstly, operating states of the equipment should be precisely diagnosed in real-time. Then, if incipient fault symptoms are detected, preventive actions (e.g., repairing the whole rotating system or replacing degraded components with new ones) are taken. Therefore, fault detection and diagnosis (FDD) techniques are a prerequisite for more successful execution of CBM for the rotating machinery. The FDD techniques are generally divided into physics model-based and data-driven approaches. Physics model-based approaches rely on rigorously designed mathematical models to describe the dynamics of the rotating machinery. In the mathematical model, the inconsistency value between the model's outputs and system measurements is computed, and it quantifies the level of rotating machinery faults [6]. Because of their theoretical rigorousness, the physics model-based approaches are only available when assumptions regarding the dynamics of the rotating machinery are satisfied [7]. However, as for the complex and sophisticated modern rotating machinery, it is difficult to properly formulate mathematical models to describe their dynamics, and the parameters of those mathematical models are also not accurately estimated [8]. In contrast, data-driven approaches do not require any assumption on the dynamics of real machinery systems. Furthermore, these approaches can achieve robust and reliable FDD performance when sufficient operational data are available and appropriate analytical techniques are utilized [8]. In recent years, owing to the rapid development of data acquisition and storage techniques, industrial systems have collected enormous operational data of machinery equipment in real-time. Especially, rolling bearings are fundamental components in these industrial machines, and this CBM primarily relies on monitoring vibration signals of the rolling bearings. Hence, in this study, we utilize the signal data collected from the rolling bearings [9]. Recent studies regarding FDD have utilized signal data obtained from various sensors, including vibration, acoustic, and thermometric channels. Among them, vibration signals are the most frequently used because information on operating states of the rotating machinery is directly reflected in vibration signals [10; 11; 12]. Nevertheless, the raw vibration signals should be simplified by summarizing them into several features because they are not easy to directly analyze. To more properly facilitate the vibration signal data, meaningful features of the signals should be extracted to identify their inherent properties. Accordingly, feature extraction is an essential procedure in signal-data-driven FDD for rotating machinery.