Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics

• URL: http://arxiv.org/abs/2501.05087v1
• Date: Thu, 09 Jan 2025 09:11:40 GMT
• Title: Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics
• Authors: David J Poland,
• Abstract summary: This paper presents a novel predictive maintenance framework centered on Enhanced Quantile Regression Neural Networks EQRNNs. We address the challenge of early failure detection through a hybrid approach that combines advanced neural architectures. The framework's effectiveness in processing complex, multi-modal sensor data while maintaining computational efficiency validates its applicability for Industry 4.0 manufacturing environments.
• Score: 0.0
• License:
• Abstract: This paper presents a novel predictive maintenance framework centered on Enhanced Quantile Regression Neural Networks EQRNNs, for anticipating system failures in industrial robotics. We address the challenge of early failure detection through a hybrid approach that combines advanced neural architectures. The system leverages dual computational stages: first implementing an EQRNN optimized for processing multi-sensor data streams including vibration, thermal, and power signatures, followed by an integrated Spiking Neural Network SNN, layer that enables microsecond-level response times. This architecture achieves notable accuracy rates of 92.3\% in component failure prediction with a 90-hour advance warning window. Field testing conducted on an industrial scale with 50 robotic systems demonstrates significant operational improvements, yielding a 94\% decrease in unexpected system failures and 76\% reduction in maintenance-related downtimes. The framework's effectiveness in processing complex, multi-modal sensor data while maintaining computational efficiency validates its applicability for Industry 4.0 manufacturing environments.

Related papers

• AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence [65.29835430845893]
  We propose a framework that enhances edge intelligence through AI-in-the-loop joint sensing and communication. A key contribution of our work is establishing an explicit relationship between validation loss and the system's tunable parameters. We show that our framework reduces communication energy consumption by up to 77 percent and sensing costs measured by the number of samples by up to 52 percent.
  arXiv  Detail & Related papers  (2025-02-14T14:56:58Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks. embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. split computing - where an SNN is partitioned across two devices - is a promising solution. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• Industrial Machines Health Prognosis using a Transformer-based Framework [0.0]
  This article introduces Transformer Quantile Regression Neural Networks (TQRNNs) TQRNNs are a novel data-driven solution for real-time machine failure prediction in manufacturing contexts. Our findings demonstrate the model's effectiveness, achieving an accuracy rate of 70.84% with a 1-hour lead time for predicting machine breakdowns.
  arXiv  Detail & Related papers  (2024-11-05T18:47:05Z)
• Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks [1.7333836118546833]
  We present a novel spiking neural network model enhanced with active dendrites. Our model can efficiently mitigate catastrophic forgetting in temporally-encoded SNNs. We provide a novel digital hardware architecture that paves the way for real-world deployment in edge devices.
  arXiv  Detail & Related papers  (2024-04-30T10:11:03Z)
• Linear Combination of Exponential Moving Averages for Wireless Channel Prediction [2.34863357088666]
  In this work, prediction models based on the exponential moving average (EMA) are investigated in depth. A new model that we called EMA linear combination (ELC) is introduced, explained, and evaluated experimentally.
  arXiv  Detail & Related papers  (2023-12-13T07:44:05Z)
• CorrectNet: Robustness Enhancement of Analog In-Memory Computing for Neural Networks by Error Suppression and Compensation [4.570841222958966]
  We propose a framework to enhance the robustness of neural networks under variations and noise. We show that inference accuracy of neural networks can be recovered from as low as 1.69% under variations and noise.
  arXiv  Detail & Related papers  (2022-11-27T19:13:33Z)
• Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs [74.83613252825754]
  "smart ecosystems" are being formed where sensing happens concurrently rather than standalone. This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge. We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
  arXiv  Detail & Related papers  (2022-09-27T15:04:01Z)
• Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks [2.0738462952016232]
  We propose a low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to SOTA CNN architectures. Our network achieves cross validation accuracy of 99.84% for epileptic seizure detection and 97.54% for epileptic seizure prediction. The CNN component of our platform is estimated to consume approximately 2.791W of power while occupying an area of 31.255mm$2$ in a 22nm FDSOI CMOS process.
  arXiv  Detail & Related papers  (2022-06-20T18:16:35Z)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks [61.76338096980383]
  A range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper- parameters of state-of-the-art factored time delay neural networks (TDNNs) These include the DARTS method integrating architecture selection with lattice-free MMI (LF-MMI) TDNN training. Experiments conducted on a 300-hour Switchboard corpus suggest the auto-configured systems consistently outperform the baseline LF-MMI TDNN systems.
  arXiv  Detail & Related papers  (2020-07-17T08:32:11Z)

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.