A Comparison of Residual-based Methods on Fault Detection

• URL: http://arxiv.org/abs/2309.02274v1
• Date: Tue, 5 Sep 2023 14:39:27 GMT
• Title: A Comparison of Residual-based Methods on Fault Detection
• Authors: Chi-Ching Hsu, Gaetan Frusque, Olga Fink
• Abstract summary: In this study, we compare two residual-based approaches to detect faults in industrial systems. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate.
• Score: 6.675805308519987
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over time, and isolate faults. As faults are typically rare occurrences, it is essential to perform this monitoring in an unsupervised manner. Various approaches have been proposed not only to detect faults in an unsupervised manner but also to distinguish between different potential fault types. In this study, we perform a comprehensive comparison between two residual-based approaches: autoencoders, and the input-output models that establish a mapping between operating conditions and sensor readings. We explore the sensor-wise residuals and aggregated residuals for the entire system in both methods. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. To perform the comparison, we utilize the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model, specifically a subset of the turbofan engine dataset containing three different fault types. All models are trained exclusively on healthy data. Fault detection is achieved by applying a threshold that is determined based on the healthy condition. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate. While the fault detection performance is similar for both models, the input-output model provides better interpretability regarding potential fault types and the possible faulty components.

Related papers

• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Fault Detection and Monitoring using an Information-Driven Strategy: Method, Theory, and Application [5.056456697289351]
  We propose an information-driven fault detection method based on a novel concept drift detector. The method is tailored to identifying drifts in input-output relationships of additive noise models. We prove several theoretical properties of the proposed MI-based fault detection scheme.
  arXiv  Detail & Related papers  (2024-05-06T17:43:39Z)
• Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework [2.7898966850590625]
  This paper introduces a new method for fault detection and condition monitoring for unseen data. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise.
  arXiv  Detail & Related papers  (2024-04-08T22:20:23Z)
• An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators [0.0]
  Inspection of insulators is important to ensure reliable operation of the power system. Deep learning is being increasingly exploited to automate the inspection process. This article proposes the use of anomaly detection along with object detection in a two-stage approach for incipient fault detection.
  arXiv  Detail & Related papers  (2023-11-14T11:36:20Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Zero-Shot Motor Health Monitoring by Blind Domain Transition [17.664784126708742]
  We propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.
  arXiv  Detail & Related papers  (2022-12-12T18:36:02Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Tracking the risk of a deployed model and detecting harmful distribution shifts [105.27463615756733]
  In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate.
  arXiv  Detail & Related papers  (2021-10-12T17:21:41Z)
• An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery [2.055054374525828]
  This paper proposes a new approach for fault detection and diagnosis in rotating machinery. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults.
  arXiv  Detail & Related papers  (2021-02-23T18:28:18Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.