Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

Liu, Jingjing; Zhang, Min; Wang, Hai; Zhao, Wei; Liu, Yan

doi:10.1155/2019/5367217

Cited by 20 publications

(10 citation statements)

References 28 publications

(30 reference statements)

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“…An example of this fact is the case of [15], where anomalies, defined as unusual behaviour at a specific time, were detected by using Hierarchical Temporal Memory. Another interesting example can be seen in [16], where a Convolutional Neural Network (CNN) was developed in order to detect sensor failures. An example of the application of NNs for anomaly detection in power plants can be found in [17].…”

Section: Related Work and Literature Reviewmentioning

confidence: 99%

Real-time detection of uncalibrated sensors using neural networks

Muñoz-Molina

Cazorla-Piñar²,

Domínguez-Morales

et al. 2022

Neural Comput & Applic

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Nowadays, sensors play a major role in several fields, such as science, industry and everyday technology. Therefore, the information received from the sensors must be reliable. If the sensors present any anomalies, serious problems can arise, such as publishing wrong theories in scientific papers, or causing production delays in industry. One of the most common anomalies are uncalibrations. An uncalibration occurs when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors is presented. This development integrates an artificial neural network as the main component which learns from the behavior of the sensors under calibrated conditions. Then, after being trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed system is able to detect the 100% of the presented uncalibration events, although the time response in the detection depends on the resolution of the model for the specific location, i.e., the minimum statistically significant variation in the sensor behavior that the system is able to detect. This architecture can be adapted to different contexts by applying transfer learning, such as adding new sensors or having different environments by re-training the model with minimum amount of data.

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Section: Related Work and Literature Reviewmentioning

confidence: 99%

Real-time detection of uncalibrated sensors using neural networks

Muñoz-Molina

Cazorla-Piñar²,

Domínguez-Morales

et al. 2022

Neural Comput & Applic

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show abstract

“…[6] presented an online strategy based on the principal component analysis (PCA) to detect, diagnose and validate sensor faults in centrifugal chillers. Other related investigations can be found in [7,8,9,26,27,28]. However, these works focus on fault detection and diagnosis, not fault-tolerant control.…”

Section: B Addressing Sensor Faults In Buildingsmentioning

confidence: 99%

Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning

Xu¹,

Wang³

et al. 2021

Preprint

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As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. Those HVAC systems in modern smart buildings rely on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learningbased framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive numerical experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

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“…In this work, CNN was chosen over the other approaches because of its superior region feature extraction capabilities and unique model parameter sharing mechanism [ 24 ]. Many experts and researchers have conducted extensive research on CNN models.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Cai

Wang

Yao

et al. 2022

Computational Intelligence and Neuroscience

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Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis framework employs a model training strategy based on early stopping optimization to ensemble several one-dimensional dilated convolutional neural networks (1D-DCNNs). By varying the dilation rate of the 1D-DCNN, different receptive fields can be obtained to extract different vibration signal features. The early stopping strategy is used as a model update threshold to prevent overfitting and save computational resources. Ensemble learning uses a weighted mechanism to combine the outputs of multiple 1D-DCNN subclassifiers with different dilation rates to obtain the final fault diagnosis. The proposed method outperforms existing state-of-the-art classical machine learning and deep learning methods in simulation studies and diagnostic experiments, demonstrating that it can thoroughly mine fault features in vibration signals. The classification results further show that the EDCNN model can effectively and accurately identify multiple faults and outperform existing fault detection techniques.

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Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

Cited by 20 publications

References 28 publications

Real-time detection of uncalibrated sensors using neural networks

Real-time detection of uncalibrated sensors using neural networks

Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

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