# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 1 Introduction  
Indisputably, rotary machinery is broadly utilized across production industries such as in power systems, petrochemicals and means of transportation due to its low cost, rugged high efficiency under a heavy load, reliability, and robust design. Generally, the consequence of rotary machinery obliged to operate for prolonged periods under harsh-condition environments is wear and tear, which is associated with mechanical stresses, which can lead to unexpected failure in bearings and gears, which are crucial components in a rotary machine. Such failures could lead to economic losses or human casualties. As a consequence, the machine's health supervision and fault analysis are vital integral elements of the maintenance procedure in industrial manufacturing. A robustly conditioned monitoring procedure can improve productivity, reduce maintenance expenses, and enhance reliability and safety. While gear and bearing faults commonly betide the rotary machine, bearing faults prevail in occurrence. Industrial statistics illustrate that 40% of total large machine breakdowns happened due to broken bearings while for small machines, the analogous number reaches up to 90% [1]. Therefore, real-time monitoring and fault diagnosis methods for rolling element bearings haveaccrued considerable attention from researchers in recent years. Normally, there are three primary categories for fault diagnosis methods including reactive, preventive, and predictive maintenance [2]. Fault diagnosis methods can also be categorized as data-driven-based (knowledge-based), model-based, and hybrid-based with the hybrid-based method considered as the combination of one or several methods of model-based and data-driven-based [3; 4; 5]. In model-based fault diagnosis, the bearing system is analyzed by constructing an equivalent mathematical model that describes the differences between the normal state and fault states; however, the model-based method can be complicated by the increasing non-linearity of the system. The computational cost increases when the complexity of the system increases [6; 7]. Data-driven-based methods are based on measurements taken over time and with data analysis to yield an assessment about the physical state of the machine [8; 9]. With the developments in acquisition devices and sensors, communication technology, availability of data such as big data and cloud computing, and effective data processing methods, data-driven-based fault diagnosis has emerged as the most suitable fault diagnosis option. Bearing fault data can be acquired from acoustic emission measurements, ultrasound, vibration, temperature, thermal images, and current sources, all of which are extensively applied for investigation. Previously, fault analysis by vibration signals attracted more attention for supervising the health of the machine thanks to its potential to transmit the intrinsic information of the rotary machine. Using acoustic emission (AE) signals for fault diagnosis offers some advantages over the vibration signal when applied to rotary machines. For instance, the vibration-based signal is susceptible to presenting degradation of bearings at low rotation speeds, and the signal is not appropriate to detect and isolate faults that are still in the incipient stage. To identify damage before it manifests in the form of small cracks on the metal surface, AE signal-based methods have proven to be effective at comparatively low speeds. The classification or identification of a fault is impossible when analyzed from the raw fault data. With a diversity of signal processing methods, raw sensor data are processed to extract the information that correlates to the faults. Some well-established approaches for signal processing are analysis with time-domain techniques, frequency-domain techniques, and time-frequency-domain techniques.