Fault Detection

Ask about this topic

Overview

Fault detection studies how to recognize departures from normal operation and, increasingly, identify their source, severity, or likely consequences. Common approaches include statistical residuals and change-point tests, supervised classifiers, reconstruction-based anomaly detection, and temporal models trained on sensor, audio, image, or system-log data; physics-based models and domain knowledge can make their decisions easier to verify. Methods are tested on settings ranging from rotating machinery and power grids to industrial inspection, spacecraft telemetry, and software agents, with growing attention to online monitoring and edge deployment. A central challenge is detecting rare or unfamiliar faults while keeping false alarms low under changing operating conditions, and strong detection scores do not always translate into reliable fault localization or recovery.

Latest papers

CardsList

105 papers13 papers in the last four weeks, down 7% on the four weeks before. 0.1% of all new papers.

Mar 13, 2026

On Using Machine Learning to Early Detect Catastrophic Failures in Marine Diesel Engines

Catastrophic failures of marine engines imply severe loss of functionality and destroy or damage the systems irreversibly. Being sudden and often unpredictable events, they pose a severe threat to navigation, crew, and passengers. The abrupt nature makes early detection the only effective countermeasure. However, research has concentrated on modeling the gradual degradation of components, with limited attention to sudden and anomalous phenomena. This work proposes a new method for early detection of catastrophic failures. Based on real data from a failed engine, the approach evaluates the derivatives of the deviation between actual sensor readings and expected values of engine variables. Predictions are obtained by a Random Forest, which is the most suitable Machine Learning algorithm among the tested ones. Traditional methods focus on deviations of monitored signals, whereas the proposed approach employs the derivatives of the deviations to provide earlier indications of abnormal dynamics, and to alert that a rapid and dangerous event is breaking out within the system. The method allows the detection of anomalies before measurements reach critical thresholds and alarms are triggered, which is the common method in industry. Consequently, operators can be warned in advance and shut down the engine, then prevent damage and unexpected power loss. Moreover, they have the time to safely change the ship route and avoid potential obstacles. Simulation results conf irm the effectiveness of the proposed approach in anticipating occurrence of catastrophic failures. Validation on real-world data further reinforces the robustness and practical applicability of the method. It is worth noting that data acquisition to train the predictive algorithm is not a problem, since a Deep Learning-based data augmentation procedure is used.

Francesco Maione, Paolo Lino, Giuseppe Giannino +1

Fault DetectionTime-Series Anomaly Detection
AskPDF
Mar 13, 2026

Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains

Accurate visual fault detection in freight trains remains a critical challenge for intelligent transportation system maintenance, due to complex operational environments, structurally repetitive components, and frequent occlusions or contaminations in safety-critical regions. Conventional instance segmentation methods based on convolutional neural networks and Transformers often suffer from poor generalization and limited boundary accuracy under such conditions. To address these challenges, we propose a lightweight self-prompted instance segmentation framework tailored for freight train fault detection. Our method leverages the Segment Anything Model by introducing a self-prompt generation module that automatically produces task-specific prompts, enabling effective knowledge transfer from foundation models to domain-specific inspection tasks. In addition, we adopt a Tiny Vision Transformer backbone to reduce computational cost, making the framework suitable for real-time deployment on edge devices in railway monitoring systems. We construct a domain-specific dataset collected from real-world freight inspection stations and conduct extensive evaluations. Experimental results show that our method achieves 74.6 APboxAP^{\text{box}} and 74.2 APmaskAP^{\text{mask}} on the dataset, outperforming existing state-of-the-art methods in both accuracy and robustness while maintaining low computational overhead. This work offers a deployable and efficient vision solution for automated freight train inspection, demonstrating the potential of foundation model adaptation in industrial-scale fault diagnosis scenarios. Project page: https://github.com/MVME-HBUT/SAM_FTI-FDet.git

Guodong Sun, Qihang Liang, Xingyu Pan +2

the School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China · The Hubei Key Laboratory of Modern Manufacturing Quality Engineering, Hubei University of Technology, Wuhan 430068, China · the School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Fault DetectionInstance SegmentationVision Foundation Model AdaptationPromptable Image Segmentation
AskPDF
Mar 11, 2026

Towards Cognitive Defect Analysis in Active Infrared Thermography with Vision-Text Cues

Active infrared thermography (AIRT) is currently witnessing a surge of artificial intelligence (AI) methodologies being deployed for automated subsurface defect analysis of high performance carbon fiber-reinforced polymers (CFRP). Deploying AI-based AIRT methodologies for inspecting CFRPs requires the creation of time consuming and expensive datasets of CFRP inspection sequences to train neural networks. To address this challenge, this work introduces a novel language-guided framework for cognitive defect analysis in CFRPs using AIRT and vision-language models (VLMs). Unlike conventional learning-based approaches, the proposed framework does not require developing training datasets for extensive training of defect detectors, instead it relies solely on pretrained multimodal VLM encoders coupled with a lightweight adapter to enable generative zero-shot understanding and localization of subsurface defects. By leveraging pretrained multimodal encoders, the proposed system enables generative zero-shot understanding of thermographic patterns and automatic detection of subsurface defects. Given the domain gap between thermographic data and natural images used to train VLMs, an AIRT-VLM Adapter is proposed to enhance the visibility of defects while aligning the thermographic domain with the learned representations of VLMs. The proposed framework is validated using three representative VLMs; specifically, GroundingDINO, Qwen-VL-Chat, and CogVLM. Validation is performed on 25 CFRP inspection sequences with impacts introduced at different energy levels, reflecting realistic defects encountered in industrial scenarios. Experimental results demonstrate that the AIRT-VLM adapter achieves signal-to-noise ratio (SNR) gains exceeding 10 dB compared with conventional thermographic dimensionality-reduction methods, while enabling zero-shot defect detection with intersection-over-union values reaching 70%.

Mohammed Salah, Eman Ouda, Giuseppe Dell'Avvocato +6

Fault DetectionThermal ImagingZero-Shot Anomaly DetectionModality Gap in VLMs
AskPDF
Mar 9, 2026

Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such ''healthy'' changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory.

Natalia Wojak-Strzelecka, Szymon Bobek, Grzegorz J. Nalepa +1

1Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. prof. Stanisława Łojasiewicza 11, 30-348 Krakow, Poland · Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland

Industrial Anomaly DetectionFault DetectionDistribution Shift DetectionChange-Point Detection
AskPDF
Mar 7, 2026

Toward Multimodal Industrial Fault Analysis: A Single-Speed Chain Conveyor Dataset with Audio and Vibration Signals

We introduce a multimodal industrial fault analysis dataset collected from a single-speed chain conveyor (SSCC) system, targeting system-level fault detection in production lines. The dataset consists of multimodal signals, including three audio and four vibration channels. It covers normal operation and four representative fault types under multiple speeds, loads, and both clean and realistic factory-noise conditions reproduced on-site. It is explicitly designed to support channel-wise analysis and multimodal fusion research. We establish standardized evaluation protocols for unsupervised fault detection with normal-only training and supervised fault classification with balanced dataset splits across different operating conditions and fault types. A unified channel-wise kNN baseline is provided to enable fair comparison of representation quality without task-specific training. The dataset offers a practical and extensible benchmark for robust multimodal industrial fault analysis.

Zhang Chen, Yucong Zhang, Xiaoxiao Miao +1

Digital Innovation Research Center, Duke Kunshan University, Kunshan, China · School of Computer Science, Wuhan University, Wuhan, China · School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China +1

Industrial Fault DiagnosisFault DetectionFault DiagnosisIndustrial Anomaly DetectionMultimodal Sensor Fusion
AskPDF
Mar 6, 2026

MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells

Electroluminescence (EL) imaging is widely used to detect defects in photovoltaic (PV) modules, and machine learning methods have been applied to enable large-scale analysis of EL images. However, existing methods cannot assign multiple labels to the same pixel, limiting their ability to capture overlapping degradation features. We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of EL images. The model outputs independent probability maps for cracks, busbars, dark areas, and non-cell regions, enabling accurate co-classification of interacting features such as cracks crossing busbars. The model achieved an accuracy of 98% and has been shown to generalize to unseen datasets. This framework offers a scalable, extensible tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems.

Ojas Sanghi, Norman Jost, Benjamin G. Pierce +3

Sandia National Laboratories · Sandia National Laboratories, 1515 Eubank Blvd SE, Albuquerque, New Mexico, 87123, USA · Case Western Reserve University +3

Image SegmentationFault DetectionU-Net
AskPDF
Mar 6, 2026

Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation

Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is critical, these methods are often unsuitable due to their brittleness, and lack of explainability and interpretability. Furthermore, open-source real-world datasets containing historical failures are scarce in this domain. In this paper, we investigate an approach for predicting failures in chemical processes using symbolic machine learning and conduct a feasibility study in the context of an ethylene oxidation process. Our method builds on a state-of-the-art symbolic machine learning system capable of learning predictive models in the form of probabilistic rules from context-dependent noisy examples. This system is a general-purpose symbolic learner, which makes our approach independent of any specific chemical process. To address the lack of real-world failure data, we conduct our feasibility study leveraging data generated from a chemical process simulator. Experimental results show that symbolic machine learning can outperform baseline methods such as random forest and multilayer perceptron, while preserving interpretability through the generation of compact, rule-based predictive models. Finally, we explain how such learned rule-based models could be integrated into agents to assist chemical plant operators in decision-making during potential failures.

Julien Amblard, Niklas Groll, Matthew Tait +3

Department of Computing, Imperial College London, London, United Kingdom · Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark · ILASP Limited, London, United Kingdom

Fault DetectionInterpretable MLManufacturing
AskPDF
Mar 6, 2026

Technical Report: Automated Optical Inspection of Surgical Instruments

In the dynamic landscape of modern healthcare, maintaining the highest standards in surgical instruments is critical for clinical success. This report explores the diverse realm of surgical instruments and their associated manufacturing defects, emphasizing their pivotal role in ensuring the safety of surgical procedures. With potentially fatal consequences arising from even minor defects, precision in manufacturing is paramount.The report addresses the identification and rectification of critical defects such as cracks, rust, and structural irregularities. Such scrutiny prevents substantial financial losses for manufacturers and, more crucially, safeguards patient lives. The collaboration with industry leaders Daddy D Pro and Dr. Frigz International, renowned trailblazers in the Sialkot surgical cluster, provides invaluable insights into the analysis of defects in Pakistani-made instruments. This partnership signifies a commitment to advancing automated defect detection methodologies, specifically through the integration of deep learning architectures including YOLOv8, ResNet-152, and EfficientNet-b4, thereby elevating quality standards in the manufacturing process. The scope of this report is to identify various surgical instruments manufactured in Pakistan and analyze their associated defects using a newly developed dataset of 4,414 high-resolution images. By focusing on quality assurance through Automated Optical Inspection (AOI) tools, this document serves as a resource for manufacturers, healthcare professionals, and regulatory bodies. The insights gained contribute to the enhancement of instrument standards, ensuring a more reliable healthcare environment through industry expertise and cutting-edge technology.

Zunaira Shafqat, Atif Aftab Ahmed Jilani, Qurrat Ul Ain

Authors are with National University of Computer and Emerging Sciences (FAST-NUCES), Islamabad, Pakistan

Fault DetectionIndustrial InspectionIndustrial Anomaly Detection
AskPDF
Mar 5, 2026

Tureis: Transformer-based Unified Resilience for IoT Devices in Smart Homes

Smart-home IoT systems rely on heterogeneous sensor networks whose correctness shapes application behavior and the physical environment. However, these low-cost, resource-constrained sensors are highly prone to failure under real-world stressors. Prior methods often assume single-failure, single-resident settings, offer only failure detection rather than sensor-level localization, cover limited fault types and sensor modalities, require labels and human intervention, or impose overheads hindering edge deployment. To overcome these limitations, we propose Tureis, a self-supervised, context-aware method for failure detection and faulty-sensor localization in smart homes, designed for multi-failure, multi-resident edge settings. Tureis encodes heterogeneous binary and numeric sensor streams into compact bit-level features. It then trains a lightweight BERT-style Transformer with sensor-wise masked reconstruction over short-horizon windows, capturing spatial and short-term temporal correlations without mixing unrelated events. This self-supervised objective removes the need for labels or curated semantics. Then, at run-time, Tureis converts reconstruction residuals into sensor-level failure evidence and uses an iterative isolate-and-continue loop that masks flagged sensors, allowing other failures to surface and enabling resilient, fine-grained localization. Across five datasets with up to nine residents, Tureis improves single-failure localization F1 by +7.6%, +21.0%, and +25.0% over three strong baselines. In multi-failure scenarios with up to five faulty sensors, it further boosts localization F1 by +17.6% and +35.4% over two baselines, while the third does not extend to this setting. These gains come with minute-scale localization and an edge-friendly footprint, as a sub-megabyte model that processes each minute of data in a few milliseconds with ~0.5 GB peak memory on a Raspberry Pi 5.

Alireza Borhani, Vafa Andalibi, Bahar Asgari

University of Maryland, College Park, MD, USA · Google, Manhattan, NY, USA

Fault DetectionFault LocalizationInternet of ThingsReconstruction-Based Anomaly Detection
AskPDF
Feb 25, 2026

Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection

Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL's sequential decision-making strengths, often treating MFD as a simple guessing game (Contextual Bandits). To bridge this gap, we formulate MFD as an offline inverse reinforcement learning problem, where the agent learns the reward dynamics directly from healthy operational sequences, thereby bypassing the need for manual reward engineering and fault labels. Our framework employs Adversarial Inverse Reinforcement Learning to train a discriminator that distinguishes between normal (expert) and policy-generated transitions. The discriminator's learned reward serves as an anomaly score, indicating deviations from normal operating behaviour. When evaluated on three run-to-failure benchmark datasets (HUMS2023, IMS, and XJTU-SY), the model consistently assigns low anomaly scores to normal samples and high scores to faulty ones, enabling early and robust fault detection. By aligning RL's sequential reasoning with MFD's temporal structure, this work opens a path toward RL-based diagnostics in data-driven industrial settings.

Dhiraj Neupane, Richard Dazeley, Mohamed Reda Bouadjenek +1

Deakin University Waurn Ponds, Victoria, Australia

Fault DetectionUnsupervised Anomaly DetectionReinforcement Learning
AskPDF
Feb 25, 2026

Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin

The real-time supervision of production processes is a common challenge across several industries. It targets process component monitoring and its predictive maintenance in order to ensure safety, uninterrupted production and maintain high efficiency level. The rise of advanced tools for the simulation of physical systems in addition to data-driven machine learning models offers the possibility to design numerical tools dedicated to efficient system monitoring. In that respect, the digital twin concept presents an adequate framework that proffers solution to these challenges. The main purpose of this paper is to develop such a digital twin dedicated to fault detection and diagnosis in the context of a thermal-hydraulic process supervision. Based on a numerical simulation of the system, in addition to machine learning methods, we propose different modules dedicated to process parameter change detection and their on-line estimation. The proposed fault detection and diagnosis algorithm is validated on a specific test scenario, with single one-off parameter change occurrences in the system. The numerical results show good accuracy in terms of parameter variation localization and the update of their values.

Osimone Imhogiemhe, Yoann Jus, Hubert Lejeune +1

LS2N, LS2N - équipe SIMS, Nantes Univ - ECN · Nantes Univ., Centrale Nantes LS2N, CNRS UMR 6004 F-44000 Nantes, France · Cetim +1

Fault DetectionProcess SupervisionFault DiagnosisDigital TwinsFault Localization
AskPDF
Feb 21, 2026

Depth-Enhanced YOLO-SAM2 Detection for Reliable Ballast Insufficiency Identification

This paper presents a depth-enhanced YOLO-SAM2 framework for detecting ballast insufficiency in railway tracks using RGB-D data. Although YOLOv8 provides reliable localization, the RGB-only model shows limited safety performance, achieving high precision (0.99) but low recall (0.49) due to insufficient ballast, as it tends to over-predict the sufficient class. To improve reliability, we incorporate depth-based geometric analysis enabled by a sleeper-aligned depth-correction pipeline that compensates for RealSense spatial distortion using polynomial modeling, RANSAC, and temporal smoothing. SAM2 segmentation further refines region-of-interest masks, enabling accurate extraction of sleeper and ballast profiles for geometric classification. Experiments on field-collected top-down RGB-D data show that depth-enhanced configurations substantially improve the detection of insufficient ballast. Depending on bounding-box sampling (AABB or RBB) and geometric criteria, recall increases from 0.49 to as high as 0.80, and F1-score improves from 0.66 to over 0.80. These results demonstrate that integrating depth correction with YOLO-SAM2 yields a more robust and reliable approach for automated railway ballast inspection, particularly in visually ambiguous or safety-critical scenarios.

Shiyu Liu, Dylan Lester, Husnu Narman +2

Department of Biomedical and Electrical Engineering, Marshall University, Huntington, WV 25755, USA. · Department of Computer Science, Marshall University, Huntington, WV 25755, USA. · Department of Civil Engineering, Marshall University, Huntington, WV 25755, USA.

Fault DetectionObject DetectionIndustrial Inspection
AskPDF
Feb 18, 2026

Real time fault detection in 3D printers using Convolutional Neural Networks and acoustic signals

The reliability and quality of 3D printing processes are critically dependent on the timely detection of mechanical faults. Traditional monitoring methods often rely on visual inspection and hardware sensors, which can be both costly and limited in scope. This paper explores a scalable and contactless method for the use of real-time audio signal analysis for detecting mechanical faults in 3D printers. By capturing and classifying acoustic emissions during the printing process, we aim to identify common faults such as nozzle clogging, filament breakage, pully skipping and various other mechanical faults. Utilizing Convolutional neural networks, we implement algorithms capable of real-time audio classification to detect these faults promptly. Our methodology involves conducting a series of controlled experiments to gather audio data, followed by the application of advanced machine learning models for fault detection. Additionally, we review existing literature on audio-based fault detection in manufacturing and 3D printing to contextualize our research within the broader field. Preliminary results demonstrate that audio signals, when analyzed with machine learning techniques, provide a reliable and cost-effective means of enhancing real-time fault detection.

Muhammad Fasih Waheed, Shonda Bernadin

Electrical and Computer Engineering, Florida A&M University, Tallahassee, USA

Fault DetectionAdditive Manufacturing
AskPDF
Feb 16, 2026

Fault Detection in Electrical Distribution System using Autoencoders

In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision-making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly-based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets.

Sidharthenee Nayak, Victor Sam Moses Babu, Chandrashekhar Narayan Bhende +2

ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, 500084, India. · School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, 751013, India. · Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad, 500078, India.

Power System Fault DiagnosisFault DetectionPower SystemsReconstruction-Based Anomaly Detection
AskPDF
Sep 3, 2025

CWT-Enhanced Vibration Sensing With Time-Frequency Region Localization Using YOLO

This letter presents a CWT-enhanced vibration sensing framework for bearing fault monitoring through localized time-frequency region detection on continuous wavelet transform (CWT) spectrograms. Vibration signals are transformed into CWT spectrograms to improve the observability of weak and non-stationary fault signatures, and YOLOv9, YOLOv10, and YOLOv11 are employed to detect and identify localized fault-related energy regions in the time-frequency domain. Experiments on the CWRU, PU, and IMS datasets show that the proposed framework improves the detectability and robustness of fault-related sensing patterns compared with conventional time-series models, modern vision backbones, and short-time Fourier transform (STFT)-based representations, achieving mean average precision (mAP) values up to 99.4%, 97.8%, and 99.5%, respectively. In addition, the localized region detection framework provides a more interpretable relationship between time-frequency energy distributions and characteristic bearing fault frequencies. These results demonstrate an effective and generalizable approach for interpretable vibration sensing in noisy industrial environments.

Po-Heng Chou, Wei-Lung Mao, Ru-Ping Lin +2

Research Center for Information Technology Innovation (CITI), Academia Sinica (AS), Taipei 11529, Taiwan · Department of Electrical Engineering, National Yunlin University of Science and Technology (NYUST), Yunlin 64002, Taiwan

Fault DetectionYou Only Look OnceBearing Fault Diagnosis
AskPDF