Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

Abstract

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

Anomaly detection for Smart City applications over 5G low power wide area networks

“…The proposed solution is a novel IDS with an integrated mini-firewall for 6LoWPAN networks in order to detect malicious nodes. Moreover, in [11], an anomaly detection scheme based on sensor data has been proposed to deal with unexpected behaviors in turbomachines in the Petroleum Industry. Furthermore, in [15], a temporal clustering and anomaly detection method has been presented for a car parking IoT application in order to detect unusual events.…”

“…By identifying unusual events, malfunctions in IoT sensors can be detected and transmissions of incorrect information can be avoided, which can improve the overall Quality of Service (QoS) of the IoT application, especially in terms of reliability [9]. Detecting unexpected patterns in the data traffic is known as anomaly detection [11]. Recently, anomaly detection has attracted the attention of the research community in multiple areas, such as intrusion detection, health monitoring, preventive maintenance and fault detection [12].…”

Neural network-based data-driven modelling of anomaly detection in thermal power plant

“…Some of those techniques are: distribution-based approaches, depth-based, clustering-based, distance based technique (k-nearest neighbour), density-based, spectral decomposition (principal component analysis, PCA), and classification approaches (support vector machines (SVM), neural networks), etc. [11].…”

Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

“…Bin et al [15] presented a method that provides wavelet packets-empirical mode decomposition for characteristics extraction and MLP network for fault classification. Luis et al [16] achieved fault detection in the petroleum industry using one-class SVM. The representative features from signal processing and adaptive learning capability of machine learning algorithm can provide significant accurate results in detecting or even discriminating the latent faults.…”