A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data

Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned normal regime. The underlying assumption of these approaches is that anomaly-free data is available for training. This is, however, often not the case in real-world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorithms. In this paper we introduce a framework for a fully unsupervised refinement of contaminated training data for AD tasks. The framework is generic and can be applied to any residual-based machine learning model. We demonstrate the application of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the naive approach of training with contaminated data without refinement. Moreover, we compare it to the ideal, unrealistic reference in which anomaly-free data would be available for training. Since the approach exploits information from the anomalies, and not only from the normal regime, it is comparable and often outperforms the ideal baseline as well

Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data

Best Paper Award Lizenzangabe: CC BY 3.0 United StatesQuantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning models, which are often hard to justify or explain. Various techniques for deep learning based uncertainty estimates have been developed primarily for image classification and segmentation, but also for regression and forecasting tasks. Uncertainty quantification for anomaly detection tasks is still rather limited for image data and has not yet been demonstrated for machine fault detection in PHM applications. In this paper we suggest an approach to derive an uncertainty-informed anomaly score for regression models trained with normal data only. The score is derived using a deep ensemble of probabilistic neural networks for uncertainty quantification. Using an example of wind-turbine fault detection, we demonstrate the superiority of the uncertainty-informed anomaly score over the conventional score. The advantage is particularly clear in an "out-of-distribution" scenario, in which the model is trained with limited data which does not represent all normal regimes that are observed during model deployment

Physics informed deep learning for tracker fault detection in photovoltaic power plants

One of the main challenges for fault detection in commercial fleets of machines is the lack of annotated data from the faulty condition. The use of supervised algorithms for anomaly detection or fault diagnosis is often unrealistic in this case. One approach to overcome this challenge is to augment the available normal data by generating synthetic anomalous data that represents faulty conditions. In this paper we apply this approach to the detection of faults in the tracking system of solar panels in utility-scale photovoltaic (PV) power plants. We develop a physical model in order to augment the training data for a deep convolutional neural network. We show that the physics informed learning algorithm is capable of detecting faults in an accurate and robust manner under diverse weather conditions, outperforming a purely data-driven approach. Developing and testing the algorithm with real operational data ensures its efficient deployment for PV power plants that are monitored at string level. This in turn enables the early detection of root causes for power losses, thereby contributing to the accelerated adoption of solar energy at utility scale

Transfer learning approaches for wind turbine fault detection using deep learning

Best Paper AwardImplementing machine learning and deep learning algorithms for wind turbine (WT) fault detection (FD) based on 10-minute SCADA data has become a relevant opportunity to reduce the operation and maintenance costs of wind farms. The development of practically implementable algorithms requires addressing the issue of their scalabililty to large wind farms. Two of the main challenges here are reducing the training times and enabling training with scarce or limited data. Both of these challenges can be addressed with the help of transfer learning (TL) methods, in which a base model is trained on a source WT and the learned knowledge is transferred to a target WT. In this paper we suggest three TL frameworks designed to transfer a semi-supervised FD task between turbines. As a base model we use a Convolutional Neural Network (CNN) which has been proven to perform well on the single turbine FD task. We test the three TL frameworks for transfer between WTs from the same farm and from different farms. We conclude that for the purpose of scaling up training for large farms, a simple TL based on linear regression transformation of the target predictions is an attractive high performance solution. For the challenging task of cross-farm TL based on scarce target data we show that a TL framework using combined linear regression and error-correction CNN outperforms the other methods. We demonstrate a scheme that enables the evaluation of different TL frameworks for FD without the need for labeled faults

Fully unsupervised fault detection in solar power plants using physics-informed deep learning

Machine learning algorithms for anomaly detection often assume training with historical data gathered under normal conditions, and detect anomalies based on large residuals at inference time. In real-world applications, labelled anomaly-free data is most often unavailable. In fact, a common situation is that the training data is contaminated with an unknown fraction of anomalies or faults of the same type we aim to detect. In this case, training residual-based models with the contaminated data often leads to increased missed detections and/or false alarms. While this challenge is rather common, in particular in technical fault detection setups, it is only rarely addressed in the scientific literature. In this paper we address this problem by introducing a data refinement algorithm that is capable of cleaning the contaminated training data in a fully unsupervised manner, and apply the algorithm to a problem of fault detection in grid-scale solar power plants. The data refinement framework is based on an original physics informed deep learning classification algorithm that would require healthy data as its input, in order to generate from it synthetic faulty data and train a binary classifier. We show that in order to achieve high fault detection performance, it is essential to avoid contamination of the original healthy data with unlabelled faults. To this end, we introduce an algorithm that isolates the healthy data in a fully unsupervised manner prior to training the binary classifier. We test our algorithm with field data from an operational solar power plant which includes contamination of unlabelled faulty data and demonstrate its high performance. In addition, we demonstrate the robustness of the proposed refinement method against an increasing fraction of faults in the training data