PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level

• URL: http://arxiv.org/abs/2211.12326v1
• Date: Mon, 21 Nov 2022 06:13:12 GMT
• Title: PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level
• Authors: Prajwal BN, Harsha Yelchuri, Vishwanath Shastry and T. V. Prabhakar
• Abstract summary: We describe the construction of a smart and real-time edge-based electronic product called PreMa. PreMa is basically a sensor for monitoring the health of a Solenoid Valve (SV) It has data fidelity and measurement accuracy comparable to signals captured using high end equipment.
• Score: 0.6117371161379209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In industrial process automation, sensors (pressure, temperature, etc.), controllers, and actuators (solenoid valves, electro-mechanical relays, circuit breakers, motors, etc.) make sure that production lines are working under the pre-defined conditions. When these systems malfunction or sometimes completely fail, alerts have to be generated in real-time to make sure not only production quality is not compromised but also safety of humans and equipment is assured. In this work, we describe the construction of a smart and real-time edge-based electronic product called PreMa, which is basically a sensor for monitoring the health of a Solenoid Valve (SV). PreMa is compact, low power, easy to install, and cost effective. It has data fidelity and measurement accuracy comparable to signals captured using high end equipment. The smart solenoid sensor runs TinyML, a compact version of TensorFlow (a.k.a. TFLite) machine learning framework. While fault detection inferencing is in-situ, model training uses mobile phones to accomplish the `on-device' training. Our product evaluation shows that the sensor is able to differentiate between the distinct types of faults. These faults include: (a) Spool stuck (b) Spring failure and (c) Under voltage. Furthermore, the product provides maintenance personnel, the remaining useful life (RUL) of the SV. The RUL provides assistance to decide valve replacement or otherwise. We perform an extensive evaluation on optimizing metrics related to performance of the entire system (i.e. embedded platform and the neural network model). The proposed implementation is such that, given any electro-mechanical actuator with similar transient response to that of the SV, the system is capable of condition monitoring, hence presenting a first of its kind generic infrastructure.

Related papers

• Sensor Deprivation Attacks for Stealthy UAV Manipulation [51.9034385791934]
  Unmanned Aerial Vehicles autonomously perform tasks with the use of state-of-the-art control algorithms. In this work, we propose a multi-part. Sensor Deprivation Attacks (SDAs), aiming to stealthily impact. process control via sensor reconfiguration.
  arXiv  Detail & Related papers  (2024-10-14T23:03:58Z)
• Fault Analysis And Predictive Maintenance Of Induction Motor Using Machine Learning [0.0]
  This paper presents a machine learning model for the fault detection and classification of induction motor faults. The aim of this work is to protect vital electrical components and to prevent abnormal event progression through early detection and diagnosis. Real time data from a 0.33 HP induction motor is used to train and test the neural network.
  arXiv  Detail & Related papers  (2024-09-16T02:37:07Z)
• Electrical Grid Anomaly Detection via Tensor Decomposition [41.94295877935867]
  Previous work has shown that dimensionality reduction-based approaches can be used for accurate identification of anomalies in SCADA systems. In this work, we novelly apply the tensor decomposition method Canonical Polyadic Alternating Poisson Regression with a probabilistic framework, to identify anomalies in SCADA systems. In our experiments, we model real-world SCADA system data collected from the electrical grid operated by Los Alamos National Laboratory.
  arXiv  Detail & Related papers  (2023-10-12T18:23:06Z)
• 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)
• Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets [6.114996271792091]
  In many cases, the goal of the smart manufacturing system is to rapidly detect (or anticipate) failures to reduce operational cost and eliminate downtime. This often boils down to detecting anomalies within the sensor date acquired from the system. The smart manufacturing application domain poses certain salient technical challenges. We show that predictive failure classification can be achieved, thus paving the way for predictive maintenance.
  arXiv  Detail & Related papers  (2022-06-13T17:51:24Z)
• Predicting Power Electronics Device Reliability under Extreme Conditions with Machine Learning Algorithms [0.0]
  We have utilized machine learning algorithms to predict device reliability. To train the models, we have tested 224 power devices from 10 different manufacturers. We observed that computational models such as Gradient Boosting and LSTM encoder-decoder networks can predict power device failure with high accuracy.
  arXiv  Detail & Related papers  (2021-07-21T18:17:32Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions [43.4346415363429]
  We seek to unify techniques from control theory and machine learning to synthesize controllers that achieve safety. We define a Measurement-Robust Control Barrier Function (MR-CBF) as a tool for determining safe control inputs. We demonstrate the efficacy of MR-CBFs in achieving safety with measurement model uncertainty on a simulated Segway system.
  arXiv  Detail & Related papers  (2020-10-30T00:19:01Z)
• Runtime Safety Assurance Using Reinforcement Learning [37.61747231296097]
  This paper aims to design a meta-controller capable of identifying unsafe situations with high accuracy. We frame the design of RTSA with the Markov decision process (MDP) and use reinforcement learning (RL) to solve it.
  arXiv  Detail & Related papers  (2020-10-20T20:54:46Z)
• Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach [68.40429597811071]
  The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets. A model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment.
  arXiv  Detail & Related papers  (2020-07-07T10:00:32Z)
• Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems [1.4213973379473654]
  Cyber-physical systems benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Deep neural networks, however, introduce new types of hazards that may impact system safety. Out-of-distribution data may lead to a large error and compromise safety.
  arXiv  Detail & Related papers  (2020-01-28T17:51:07Z)

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.