Integrating Pretrained CNNs with One-Class Classifiers for Fault-Agnostic Electrical Submersible Pumps Anomaly Detection

As employed in industries, anomaly detection systems can be unstable due to the lack of training examples and often narrow feature extraction methods, both of which burden common models as abnormalities are rare and exceptionally unique. Since equipment used within oil and gas companies often tend to incur on great financial losses due to inherent unrecognized malfunctions, diagnosing components’ vibration signals beforehand becomes essential. However, as obtaining examples of a plethora of possible fault patterns can be difficult, developing a system based on the equipment’s normal behavior allows for better identification of irregularities within the signals. Additionally, as erroneous feature extraction methods can mask or dismiss a signal’s abnormal demeanor, approaching this issue with a system that has generalized knowledge over the dataset’s nature may grant a more thorough signal analysis. This paper advances the fault detection in the oil industry by investigating well-known feature extraction neural networks (such as ResNet, VGG and VGGish) together with one-class classifiers (such as Isolation Forest, Elliptic Envelope and OneClass SVM). With this approach, vibration signals are converted (using transformations like spectrogram) to images in order to leverage image-pretrained networks. Results show that features extracted with pretrained networks have similar performance to those hand-crafted with prior knowledge about the faults and are significantly better than standard statistical features. Furthermore, the audio-originated pretrained CNN, VGGish, tends to perform slightly better than those trained with natural images like ImageNET. Therefore, the proposed system can be applied as a fault-agnostic feature extraction method alternative to the problem specific hand-crafted feature extraction in one-class classification problems.