# Latent Dimensions of Auto-Encoder as Robust Features for Inter-Conditional Bearing Fault Diagnosis
## 1 Introduction  
Production lines often encounter downtime, either planned or unplanned. Though planned downtime affects the Overall Equipment Efficiency (OEE), it can be reduced by an optimized usage plan. In contrast, unplanned downtime, as the name suggests, is not planned beforehand and is typically due to the component failure, tool breakage or other technical stoppage. It is difficult to foresee some of these failures and plan the maintenance tasks accordingly. Predictive or condition-based maintenance is a solution that can be employed in manufacturing industries to reduce this unplanned downtime. Predictive maintenance methods, by predicting the condition of a machine or a component, allow dynamic and convenient maintenance scheduling, thus making it an adequate option for modern manufacturing industries [1]. Predictive maintenance, from the authors' perspective, is accomplished in three important steps: (1) monitor a machine, (2) predictif something is anomalous and the machine is prone to failure and (3) make sure it is maintained without causing any disturbance to the planned production. In situ deployed sensors take care of the monitoring aspect, whereas fault prediction and maintenance are traditionally manual procedures. This intermediate task of predicting the early faults whilst monitoring the machines has been researched for a long time [2]. Importantly, there are two types of fault diagnosis methods to monitor machines or machine components, model-based and data-driven. Though very commonly used, the mathematical model of a fault diagnosis system designed using a model-based method cannot take into account all the noises or subtle variations that a system is exposed to [3]. Hence, depicting an accurate machine model or open-source availability of such models is unfeasible, whereas using an inaccurate model may lead to poor fault diagnostic performance in practice. Unlike the model-based approaches, data-driven methods utilize the collected in-process data. These data are analyzed, and the acquired knowledge is used to build a fault diagnosis model. This allows a user to apply diagnostics even when there are no available mathematical models of a particular machine. It also includes noise and a certain degree of variations into the diagnostic model. This noise inclusion into the diagnostic model is simple, with data-driven modeling compared to the model-based method [3], thus making it a more desirable solution for fault diagnosis. Recently, data-driven methods have been performing exceptionally well in predictive fault diagnosis [4; 5; 6; 7]. One of the main reasons for the increased performance of data-driven models over model-based methods is the availability of open source datasets. Looking into the state of the art for predictive maintenance in the manufacturing industry, most data-driven approaches focus on finding bearing faults, as these are the most predominant source of machine downtime [8]. There is a lot of research that shows very good classification results for bearing fault detection on the standard datasets that exist for vibration-based rolling element faults. [PERSON] et al. show how a 1D Convolutional Neural Network (CNN) can be used on time domain data to accurately predict a bearing fault [9]. Moreover, by reshaping the time domain vibration data into a 2D array, i.e., into an image-style input, the two dimensional convolutional operations were used for feature extraction [10; 11]. This study further presented the improved accuracy of the 2D CNNs for both normal and noisy data using the time domain data as input. Unlike the above, other works have shown the effectiveness of time-frequency transformed features for bearing fault detection [12]. In their study, only a few selective features of Empirical Mode Decomposition were used along with certain Machine Learning algorithms for effective classification of bearing faults.