# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 1 Introduction
[12] developed two different artificial neural network approaches, which were the feed-forward network with back propagation algorithm and the binary adaptive resonance network for the fault detection of a centrifugal pumping system. Both models had good performance using simulated data. [PERSON] et al. [13] applied convolutional neural networks to detect refrigerant leakage in heat pumps, and the proposed method was proved effective using experimental data. However, some challenges in the fault detection of pumps still remain. 1. Based on the literature review, apart from conventional fault detection models (Shewhart control charts and its variants, principal component analysis (PCA), etc. ), state-of-the-art fault detection methods (i.e., multi-layer perceptron neural network and fuzzy techniques system [11], binary adaptive resonance network [12], convolutional neural networks [13], SAE, etc.) worked well on simulated and experimental data, lacking tests in real industrial applications. 2. Most of the aforementioned references detect faults on specific components or fault types, lack a global health indicator from a system-wide perspective. Currently, there is a general trend to construct a system-wide health monitoring system using multivariate industrial field data. 3. During the operation of modern industrial machines, a huge volume of data is collected and archived by the monitoring systems. However, most data belong to normal operating conditions, while faulty data are usually rare and sometimes cannot be obtained. Instead of classification model, it requires an anomaly detection models that are able to take full advantage of the large volume of healthy data and effectively detect the fault. 4. Again, based on the literature reviews [5; 18; 19] and input from our sponsor (Shell), conventional fault detection systems in process industries have the problem of insensitive to incipient faults or generating a large numbers of false alarms, which may incur high maintenance costs and reduce the availability of the equipment. Therefore, there is a requirement for finding an effective fault detection model with lower false alarm rates and higher fault detection rate. Meanwhile, it is necessary to identify the most relevant variables that are strongly related to the detected fault, because this fault isolation can help determine the root cause of the fault and provide decision support for operators to timely adjust pump operation and take maintenance actions if necessary. Among the Artificial Intelligence (AI)-based fault detection approaches, unsupervised machine learning methods are of great interest. In the strategy, an anomaly/fault detection model is trained using data collected when the system is in a healthy state. When new data come in, it is compared to the model's prediction and an alarm is raised to indicate an anomaly condition if an intolerable deviation is found. Principal component analysis (PCA) is one of the widely used unsupervised machine learning methods. It projects high dimensional data to a lower dimensional subspace to distinguish \"normal\" data from \"abnormal\" data. PCA has been widely applied in process control [7], fault detection and system risk assessment [20], network traffic anomaly detection [21], network intrusion detection [22], etc. More recently, autoencoder (AE) and its variants [23], which can automatically learn features from unlabeled data, are gaining popularity for anomaly detection. An autoencoder is a special type of neural networks that includes an encoder and decoder. The encoder layer(s) has similar principles as PCA, but with a slight difference that PCA is linear while the autoencoder can be nonlinear. The decoder layer(s) of AE reconstructs the input. A notable feature of the AE approach is that it can learn arbitrary relationships among different sensor variables in both linear and nonlinear cases and therefore be more flexible than existing linear monitoring methods [24]. However, like many neural networks, AE suffers from the overfitting problem [25].