# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 1 Introduction  
As an important component in the oil and petrochemical industry, pumps are widely used in different sectors, including the production line, transportation process and refinery factory. According to investigations in [1], pumps are responsible for the most equipment failures in a petrochemical plant. A minor fault in a pump can gradually develop to a severe one, which threatens the safety and productivity of plants. Therefore, detecting faults in pumps at an early stage is of great importance for engineers and managers. The fault detection of pumps belongs to a \"process monitoring and fault detection\" (PMFD) field [2; 3]. The genesis of the PMFD strategies can be traced back to the advent of the basic Shewhart control charts in the 1930s [4]. The control chart has been successfully used to date for monitoring a single variable. They are most effective for detecting large shifts in the measured variables which can indicate the health condition of the process or equipment being monitored; however, they are insensitive to smaller shifts, which may indicate an incipient fault in the concerned process or the equipment [5]. This led to the development of the improved control charts (i.e., cumulative sum control chart and exponentially weighted moving average control chart) in the 1950s, as well as model-based and data-based approaches. The model-based PMFD approach develops explicit mathematical models to realize fault detection activities of the concerned facility. Two main such approaches for pump fault detection are state observer based methods and models based on parameter estimation [3]. However, in practice, model-based approaches often fail to work in complexed equipment monitoring, due to the difficulty in accurately modelling multiple couplings in system parameters [6]. By contrast, the data-driven approaches directly apply the monitored variables to infer the faults, instead of using physical or accurate mathematical models. A distinct feature of data-driven approaches is that no prior information about the system is necessary, making it more suitable for fault detection in complex systems. Prominent among these approaches are the statistical-based [7], frequency- and time-frequency-analysis-based [8; 9; 10], and artificial intelligence (AI)-based PMFD methods [11; 12; 13]. The statistical-based PMFD methods started from the combination of control charts (i.e., Shewhart, CUSUM and EWMA control charts) with R chart, S chart, and other various charts for single variable monitoring [7]. With the advancement of data acquisition systems, measurements on a number of important process variables are now being made available. [PERSON] (1947) [14] paved the way for multivariate statistical process control method by introducing the T\({}^{2}\) statistic which became the harbinger for all the multivariate statistical process control methodologies developed henceforth. The squared prediction error (SPE), namely the Q statistics [15], were also developed for multivariate fault detection. Frequency- and time-frequency-analysis-based methods are very popular in pump fault detection. These methods have proved very effective in analysing vibration and acoustic emissions signals [8; 9; 10]. The availability of cost-effective data acquisition systems and great advancements in machine learning techniques lead to the application of artificial intelligence (AI) in PMFD, namely AI-based strategies [16; 17]. A few papers can be found on AI-based PMFD for pumps, and most of them were tested by simulated or experimental data. [PERSON] et al. [11] created a real-time fault detection tool for a centrifugal pump using multi-layer perceptron neural network and fuzzy techniques system based on vibration measurements. The system was able to detect fault modes, including partial flow rates, loosening of front/rear pump attachments, misalignment, cavitation, and air injection on the inlet, using experimental data. [PERSON] et al.