A Novel Method For Designing Transferable Soft Sensors And Its Application

• URL: http://arxiv.org/abs/2008.02186v2
• Date: Wed, 18 Nov 2020 20:58:06 GMT
• Title: A Novel Method For Designing Transferable Soft Sensors And Its Application
• Authors: Hossein Shahabadi Farahani, Alireza Fatehi, Alireza Nadali and Mahdi Aliyari Shoorehdeli
• Abstract summary: We propose a new transfer learning based regression method, called Domain Adversarial Neural Network Regression (DANN-R) for designing transferable soft sensors. We used data collected from the SCADA system of an industrial power plant to investigate the functionality of the proposed method.
• Score: 0.5735035463793008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a new approach is proposed for designing transferable soft sensors. Soft sensing is one of the significant applications of data-driven methods in the condition monitoring of plants. While hard sensors can be easily used in various plants, soft sensors are confined to the specific plant they are designed for and cannot be used in a new plant or even used in some new working conditions in the same plant. In this paper, a solution is proposed for this underlying obstacle in data-driven condition monitoring systems. Data-driven methods suffer from the fact that the distribution of the data by which the models are constructed may not be the same as the distribution of the data to which the model will be applied. This ultimately leads to the decline of models accuracy. We proposed a new transfer learning (TL) based regression method, called Domain Adversarial Neural Network Regression (DANN-R), and employed it for designing transferable soft sensors. We used data collected from the SCADA system of an industrial power plant to comprehensively investigate the functionality of the proposed method. The result reveals that the proposed transferable soft sensor can successfully adapt to new plants.

Related papers

• A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset [2.07180164747172]
  This study proposes a novel predictive model based on the Transformer, utilizing statistical feature embedding and window positional encoding. The model's performance is evaluated in two problems: fault detection and virtual metrology, showing superior results compared to baseline models. The results support the model's applicability across various manufacturing industries, demonstrating its potential for enhancing process management and yield.
  arXiv  Detail & Related papers  (2024-07-09T08:59:27Z)
• One Masked Model is All You Need for Sensor Fault Detection, Isolation and Accommodation [1.0359008237358598]
  We propose a novel framework for sensor fault detection using masked models and self-supervised learning. We validate our proposed technique on both a public dataset and a real-world dataset from offshore GE wind turbines. Our proposed technique has the potential to significantly improve the accuracy and reliability of sensor measurements in real-time.
  arXiv  Detail & Related papers  (2024-03-24T13:44:57Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data [0.0]
  In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines. We use a combination of pre-processing techniques and machine learning (ML) models with a low computational cost.
  arXiv  Detail & Related papers  (2023-10-15T18:43:45Z)
• Detection of Sensor-To-Sensor Variations using Explainable AI [2.2956649873563952]
  chemi-resistive gas sensing devices are plagued by issues of sensor variations during manufacturing. This study proposes a novel approach for detecting sensor-to-sensor variations in sensing devices using the explainable AI (XAI) method of SHapley Additive exPlanations (SHAP) The methodology is tested using artificial and realistic Ozone concentration profiles to train a Gated Recurrent Unit (GRU) model.
  arXiv  Detail & Related papers  (2023-06-19T11:00:54Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
  Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
  arXiv  Detail & Related papers  (2021-07-28T10:19:58Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• 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)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

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.