Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application

• URL: http://arxiv.org/abs/2405.03667v2
• Date: Wed, 12 Feb 2025 14:56:35 GMT
• Title: Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application
• Authors: Camilo Ramírez, Jorge F. Silva, Ferhat Tamssaouet, Tomás Rojas, Marcos E. Orchard,
• Abstract summary: We propose an information-driven fault detection method based on a novel concept drift detector.<n>The method is tailored to identifying drifts in input-output relationships of additive noise models.<n>We prove several theoretical properties of the proposed MI-based fault detection scheme.
• Score: 5.056456697289351
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The ability to detect when a system undergoes an incipient fault is of paramount importance in preventing a critical failure. Classic methods for fault detection (including model-based and data-driven approaches) rely on thresholding error statistics or simple input-residual dependencies but face difficulties with non-linear or non-Gaussian systems. Behavioral methods (e.g., those relying on digital twins) address these difficulties but still face challenges when faulty data is scarce, decision guarantees are required, or working with already-deployed models is required. In this work, we propose an information-driven fault detection method based on a novel concept drift detector, addressing these challenges. The method is tailored to identifying drifts in input-output relationships of additive noise models (i.e., model drifts) and is based on a distribution-free mutual information (MI) estimator. Our scheme does not require prior faulty examples and can be applied distribution-free over a large class of system models. Our core contributions are twofold. First, we demonstrate the connection between fault detection, model drift detection, and testing independence between two random variables. Second, we prove several theoretical properties of the proposed MI-based fault detection scheme: (i) strong consistency, (ii) exponentially fast detection of the non-faulty case, and (iii) control of both significance levels and power of the test. To conclude, we validate our theory with synthetic data and the benchmark dataset N-CMAPSS of aircraft turbofan engines. These empirical results support the usefulness of our methodology in many practical and realistic settings, and the theoretical results show performance guarantees that other methods cannot offer.

Related papers

• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts. Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data [40.00357483768265]
  This work presents a novel real concept drift detection method based on Neighbor-Searching Discrepancy. The proposed method is able to detect real concept drift with high accuracy while ignoring virtual drift. It can also indicate the direction of the classification boundary change by identifying the invasion or retreat of a certain class.
  arXiv  Detail & Related papers  (2024-05-23T04:03:36Z)
• DAGnosis: Localized Identification of Data Inconsistencies using Structures [73.39285449012255]
  Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. We use directed acyclic graphs (DAGs) to encode the training set's features probability distribution and independencies as a structure. Our method, called DAGnosis, leverages these structural interactions to bring valuable and insightful data-centric conclusions.
  arXiv  Detail & Related papers  (2024-02-26T11:29:16Z)
• 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)
• A Comparison of Residual-based Methods on Fault Detection [6.675805308519987]
  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.
  arXiv  Detail & Related papers  (2023-09-05T14:39:27Z)
• Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System [0.0]
  Traditional ICS anomaly detection methods lack transparency and interpretability. This paper proposes a two-phase dual Copula-based Outlier Detection (COPOD) method that addresses these challenges. The method is based on empirical distribution functions, parameter-free, and provides interpretability by quantifying each feature's contribution to an anomaly.
  arXiv  Detail & Related papers  (2023-04-30T18:13:40Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• Detecting Concept Drift in the Presence of Sparsity -- A Case Study of Automated Change Risk Assessment System [0.8021979227281782]
  Missing values, widely called as textitsparsity in literature, is a common characteristic of many real-world datasets. We study different patterns of missing values, various statistical and ML based data imputation methods for different kinds of sparsity. We then select the best concept drift detector given a dataset with missing values based on the different metrics.
  arXiv  Detail & Related papers  (2022-07-27T04:27:49Z)
• 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)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)
• 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)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)
• Towards Out-of-Distribution Detection with Divergence Guarantee in Deep Generative Models [22.697643259435115]
  Deep generative models may assign higher likelihood to out-of-distribution (OOD) data than in-distribution (ID) data. We prove theorems to investigate the divergences in flow-based model. We propose two group anomaly detection methods.
  arXiv  Detail & Related papers  (2020-02-09T09:54:12Z)

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.