## 1 Introduction  
Centrifugal pumps are rotating machines commonly used in several industries such as mining, urban water supply and drainage, power generation, petrochemical, food industry, and more [1]. Their function is to increase the flow velocity by transferring kinetic energy. They are susceptible to hydraulic and mechanical failures such as cavitation, discharge re-circulation, bearings failures, fatigue, and excessive vibration [2]. As this equipment can be located along the critical path of the production line, its failure can disrupt the production process. In a reciprocating compressor, a crankshaft converts rotating motion into linear motion of a piston within a cylindrical chamber to increase fluid pressure. The inlet valves open during the expansion stroke of the piston, and the outlet valves open when the desired operating pressure is reached [3]. The reciprocating compressor has a complex structure, including several vulnerable parts, and the valves can fail, producing disastrous accidents that could harm the personnel and cause high economic losses. A common approach to industrial maintenance is condition-based monitoring, which could include model-based approaches, signal processing, and data-driven-based techniques. Such techniques are performed on monitored signals. The most common monitored signals are vibration, sound, temperature, flow rate, currents, and acoustic emission [4, 5]. Data-driven fault diagnosis includes several stages: feature extraction, feature selection,and fault classification [6]. The fault detection is performed using machine learning techniques, which usually include time-domain, frequency domain, and time-frequency domain features extracted from the vibration signal [5; 7; 8; 9]. Several applications related to fault detection and classification problems have been reported concerning classical machine learning techniques. The more common approaches are Support Vector Machines (SVM) [5; 10], random forests [11], the logistic regression algorithm [3], k-Nearest Neighbors (k-NN) [12], and neural networks [13; 14; 15]. Some examples of research concerning the application of machine learning techniques to fault detection and classification in centrifugal pumps and reciprocating compressors are reported in [3; 10; 13; 16]. Application of Deep Learning (DL) techniques for fault detection and classification in pumps has been used extensively [17; 18; 19; 20; 21; 22; 23; 24]. In particular, [PERSON] et al. [19] reported an approach for classifying six different types of bearings conditions, in roller bearings of a hydraulic piston pump. The classification is performed using Deep Belief Networks (DBN) using features extracted from the raw vibration signal. The average classification accuracy for the six conditions is 97.40%. An example of detecting faults in a hydraulic axial piston pump is reported in [25], where an Extreme Learning Machine (ELM) is used for classification. The highest accuracy attained is 97.8%. Research concerning the application of CNN to the problem of fault classification in a centrifugal pump is reported in [PERSON] et al. [26]. The approach is used for classifying inner and outer race faults in bearings and clogging and broken impellers in a centrifugal pump. Acoustic signals are collected, and the Analytical Wavelet Transform (AWT) is used for pre-processing. The resultant time-scale representation is converted to gray-level images for training a CNN network with a modified cost function corresponding to an entropy-based divergence function. The approach is compared to other classification models, such as the standard CNN and SVM. The accuracy was 100% for classifying five different conditions. However, rigorous cross-validation should be reported in this research. The LSTM model is applied to the problem of fault diagnosing in a reciprocating pump in [27]. An improved Complete Ensemble Empirical Mode Decomposition (CEEMD) is used for decomposing the vibration signal.