## 1 Introduction
The singular spectral entropy is calculated from the intrinsic mode functions for constructing a feature vector. The extracted features are fed to the LSTM model for classifying four fault conditions. The accuracy was 95%. Although the following application is not concerned directly with pumps, we have decided to comment on this paper because it is related to classifying many faults using LSTM models. The research is reported in [28] and performs the classification from a raw process using LSTM. The method is validated using the Tennessee Eastman benchmark process. Three experiments are reported: the first performs the classification of five fault conditions, attaining a classification accuracy of 98.1%. The second experiment classified seven fault conditions, with a classification accuracy of 80.8%, and finally, an experiment where 21 fault conditions were classified. Although the classification accuracy for this experiment was not provided, the ROC plots suggest a classification accuracy lower than 80%. Deep learning techniques have also been used for fault classification in compressors [7; 24; 29; 30; 31; 32]. In particular, CNN has been applied for reciprocating compressor fault detection [24] by considering multi-source raw vibration signals organized as 2D data that directly feds the CNN network for classification. Four-valve conditions from the secondary cylinder: normal, breakage of the valve plate, spring damage, and valve notch were classified. The accuracy was 100%; however, only a small set of conditions was considered, the research does not report formal cross-validation, and a complex CNN architecture is used. In [PERSON] et al. [29], the vibration signal is acquired from a reciprocating compressor, and a 1-DeepCNN is used for fault diagnosis. A fusion approach was applied using a multi-channel signal fed to the CNN. An accuracy of 100% was obtained for the classification of only four faulty conditions of the valves. However, the authors must have reported formal cross-validation of their approach, and the CNN architecture was complex. An application concerning the application of CNN for fault detection in a centrifugal compressor was reported in [30]. In this case, shaft orbit images obtained from turbo-machine vibration signals were fed to the CNN for fault classification. The images were pre-processed using a multi-resolution discrete wavelet packet transform and Bayesian hypothesis testing-based automatic thresholding to reduce noise's effect. The approach was used for classifying four different fault conditions. A method for fault diagnosis in reciprocating compressors using Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE) was reported in [31], a classification accuracy higher than 92.72% was reached depending on the signal-to-noise ratio (SNR). In [7], a DL approach was proposed for diagnosing faults in a two-stage reciprocating compressor's intake/discharge valves. The vibration signal was pre-processed by calculating a sequence of condition indicators. Such a sequence was used for feeding a Long Short-Term Memory (LSTM) network-based classifier [32], where the hyperparameters were estimated using Bayesian Optimization. The algorithm was validated over a dataset of vibration signals representing 17 different faulty conditions. An average accuracy of 93% was obtained. Previous research has shown the feasibility of classifying nine pitting fault severity levels in gearboxes using an LSTM with MFCC as input features extracted from acoustic emission and vibration signals [33]. Although the features are similar to the present research, the pitting severity in gearboxes is a fault detection problem different from the problem investigated in this paper. MFCC for fault diagnosis in axial piston pumps has also been reported in [20]. This approach was used for extracting features from sound signals. An ELM processes such features for classifying four types of faults; the authors perform the MFCC calculation using audio signals after applying a denoising pre-processing.