Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks

• URL: http://arxiv.org/abs/2412.05901v1
• Date: Sun, 08 Dec 2024 11:37:23 GMT
• Title: Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks
• Abstract summary: Condition monitoring of induction machines is crucial to prevent costly interruptions and equipment failure.<n>We propose using 2-dimensional Self-Organized Operational Neural Networks (Self-ONNs) to diagnose misalignment and broken rotor faults from thermal images of squirrel-cage induction motors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Condition monitoring of induction machines is crucial to prevent costly interruptions and equipment failure. Mechanical faults such as misalignment and rotor issues are among the most common problems encountered in industrial environments. To effectively monitor and detect these faults, a variety of sensors, including accelerometers, current sensors, temperature sensors, and microphones, are employed in the field. As a non-contact alternative, thermal imaging offers a powerful monitoring solution by capturing temperature variations in machines with thermal cameras. In this study, we propose using 2-dimensional Self-Organized Operational Neural Networks (Self-ONNs) to diagnose misalignment and broken rotor faults from thermal images of squirrel-cage induction motors. We evaluate our approach by benchmarking its performance against widely used Convolutional Neural Networks (CNNs), including ResNet, EfficientNet, PP-LCNet, SEMNASNet, and MixNet, using a Workswell InfraRed Camera (WIC). Our results demonstrate that Self-ONNs, with their non-linear neurons and self-organizing capability, achieve diagnostic performance comparable to more complex CNN models while utilizing a shallower architecture with just three operational layers. Its streamlined architecture ensures high performance and is well-suited for deployment on edge devices, enabling its use also in more complex multi-function and/or multi-device monitoring systems.

Related papers

• Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing [0.0]
  We propose a novel approach for generating datasets for industrial applications based on randomized physics-based simulations.<n>A neural network (NN) trained only on such a synthetic dataset is used to reconstruct the internal temperature field from sparse sensors embedded in the hardware.<n>The NN-based reconstructions do not only outperform Kriging in robustness but also enable real-time inference, making the method suitable for online monitoring of otherwise unobservable thermal states.
  arXiv  Detail & Related papers  (2026-06-03T08:16:33Z)
• A Real-Time On-Device Defect Detection Framework for Laser Power-Meter Sensors via Unsupervised Learning [1.1470070927586018]
  The system employs an unsupervised anomaly detection framework that trains exclusively on good'' sensor images to learn normal coating distribution patterns.<n>The system provides potential annual cost savings through automated quality control and processing times of 0.5 seconds per image in on-device implementation.
  arXiv  Detail & Related papers  (2025-09-25T09:29:20Z)
• Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models [0.0]
  We address interference in feature maps, a phenomenon associated with superposition, where neurons simultaneously encode multiple characteristics.<n>Our research suggests that limiting interference can enhance scaling and accuracy in very low-scaled networks (under 1.5M parameters)<n>We propose a proof-of-concept architecture named NoDepth Bottleneck built on mechanistic insights from our experiments, demonstrating robust scaling accuracy on the ImageNet dataset.
  arXiv  Detail & Related papers  (2025-07-21T16:57:25Z)
• Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection [42.021851148914145]
  Event-based Vision Sensors (EVS) have demonstrated significant advantages over traditional RGB frame-based cameras in low-light conditions.<n>This paper proposes a novel dynamic graph induced contour-aware heat conduction network for event stream based object detection.
  arXiv  Detail & Related papers  (2025-05-19T09:44:01Z)
• Localization of Impacts on Thin-Walled Structures by Recurrent Neural Networks: End-to-end Learning from Real-World Data [45.9982965995401]
  Impacts on thin-walled structures excite Lamb waves, which can be measured with piezoelectric sensors.<n>We propose to use recurrent neural networks (RNNs) to estimate impact positions end-to-end, directly from sequential sensor data.<n>Our results show remarkable accuracy in estimating impact positions, even with a comparatively small dataset.
  arXiv  Detail & Related papers  (2025-05-13T09:08:47Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data [4.392337343771302]
  We propose a system that fuses already processed multi-sensor data into a new Deep Neural Network to increase its classification accuracy towards UAV detection. The model fuses high-level features extracted from individual object detection and classification models associated with thermal, optronic, and radar data.
  arXiv  Detail & Related papers  (2024-10-21T15:12:37Z)
• Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors [41.94295877935867]
  This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft. A multi-channel Convolutional Neural Network (CNN) is used to perform multi-target classification and independently detect faults in the sensors. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level.
  arXiv  Detail & Related papers  (2024-10-11T09:36:38Z)
• Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline [5.8859061623552975]
  We present a smart vision sensor System on Chip (SoC), featuring an event-based camera and a low-power asynchronous spiking Convolutional Neural Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. We present the asynchronous architecture, the individual blocks, and the sCNN processing principle and benchmark against other sCNN capable processors.
  arXiv  Detail & Related papers  (2023-04-13T19:28:57Z)
• A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection [0.0]
  Overloading in DC servo motors is a major concern in industries, as many companies face the problem of finding expert operators. This paper proposed an embedded Artificial intelligence approach using a Convolutional Neural Network (CNN) using a new transformation to extract faults from real-time input signals without human interference.
  arXiv  Detail & Related papers  (2023-04-08T13:36:33Z)
• PCBDet: An Efficient Deep Neural Network Object Detection Architecture for Automatic PCB Component Detection on the Edge [48.7576911714538]
  PCBDet is an attention condenser network design that provides state-of-the-art inference throughput. It achieves superior PCB component detection performance compared to other state-of-the-art efficient architecture designs.
  arXiv  Detail & Related papers  (2023-01-23T04:34:25Z)
• Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference [4.158182639870093]
  Low-resolution infrared (IR) sensors can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. We propose an energy-efficient adaptive inference solution consisting of a cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN) We show that, when processing the output of a 8x8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach.
  arXiv  Detail & Related papers  (2022-04-22T07:07:38Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Towards Online Monitoring and Data-driven Control: A Study of Segmentation Algorithms for Laser Powder Bed Fusion Processes [83.97264034062673]
  An increasing number of laser powder bed fusion machines use off-axis infrared cameras to improve online monitoring and data-driven control capabilities. We study over 30 segmentation algorithms that segment each infrared image into a foreground and background. The identified algorithms can be readily applied to the laser powder bed fusion machines to address each of the above limitations and thus, significantly improve process control.
  arXiv  Detail & Related papers  (2020-11-18T03:30:16Z)
• MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System [44.76768683036822]
  We propose a real-time consistent multi-IMU multi-camera (CMU)-VINS estimator for visual-inertial navigation systems. Within an efficient multi-state constraint filter, the proposed MIMC-VINS algorithm optimally fuses asynchronous measurements from all sensors. The proposed MIMC-VINS is validated in both Monte-Carlo simulations and real-world experiments.
  arXiv  Detail & Related papers  (2020-06-28T20:16:08Z)
• Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks [40.44712305614071]
  We present Spike-FlowNet, a deep hybrid neural network architecture integrating SNNs and ANNs for efficiently estimating optical flow from sparse event camera outputs. The network is end-to-end trained with self-supervised learning on Multi-Vehicle Stereo Event Camera (MVSEC) dataset.
  arXiv  Detail & Related papers  (2020-03-14T20:37:21Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)
• Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection [0.18459705687628122]
  We focus on the inference of neural networks running in microcontrollers and low-power processors. We adapted an existing convolutional-recurrent neural network to detect and classify cardiac arrhythmias. We show our implementation in fixed-point precision, using the CMSIS-NN libraries, with a memory footprint of 195.6KB, and a throughput of 33.98MOps/s.
  arXiv  Detail & Related papers  (2020-01-08T10:35:48Z)

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.