An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems

Li, Guannan; Yao, Qing; Fan, Cheng; Zhou, Chunlin; Wu, Guanghai; Zhou, Zhongshi; Fang, Xi

doi:10.1016/j.buildenv.2021.108057

Cited by 88 publications

(23 citation statements)

References 91 publications

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“…Some of the most used supervised modeling techniques in the HVAC field are Multi-Layer Perceptrons (MLPs) [ 51 , 56 , 61 , 83 ] and their specific deep learning (DL) derivatives, such as Convolutional Neural Networks (CNNs) [ 47 , 60 , 72 , 82 , 84 ] and Recurrent Neural Networks (RNNs) [ 57 ]. There are reports of combining MLPs with other ML models, such as regression trees [ 61 ].…”

Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

“…The proposed approach eliminates the need for advanced data preprocessing and is computationally efficient. Furthermore, to overcome the disadvantage of DL black-box model interpretability, Li et al [ 60 ] proposed a novel explainable DL-based fault diagnosis method suitable for HVAC systems. CNNs have been improved to be multi-scale and provide better feature extraction capabilities for FDD of RLT devices [ 47 ].…”

Section: Results Part Ii: State-of-the-art Techniques Used In Hvac Fd...mentioning

confidence: 99%

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A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

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Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

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Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

Section: Results Part Ii: State-of-the-art Techniques Used In Hvac Fd...mentioning

confidence: 99%

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…For chiller malfunction detection systems, Srinivasan et al [15] showed the rank of understandable AI (XAI). One-dimensional convolutional neural networks (CNNs) were created by Li et al [16] for defect identification in HVAC systems. Other M&E service systems have datadriven FDD approaches proposed in addition to the interpretability research for HVAC systems.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, to get any meaningful information from the cleaned IoT sensor data, the raw sensor data must be cleaned [12,13]. A constrained IoT sensor network can also lead to high computational expenses and overuse of resources because of the vast amount of unwanted and worthless data [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Using Data Fusion with Ensemble Deep Learning Technique in IIoT

Venkatasubramanian¹,

Raja

et al. 2022

Mathematical Problems in Engineering

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Detecting the breakdown of industrial IoT devices is a major challenge. Despite these challenges, real-time sensor data from the industrial internet of things (IIoT) present several advantages, such as the ability to monitor and respond to events in real time. Sensor statistics from the IIoT can be processed, fused with other data sources, and used for rapid decision-making. The study also discusses how to manage denoising, missing data imputation, and outlier discovery using preprocessing. After that, data fusion techniques like the direct fusion technique are used to combine the cleaned sensor data. Fault detection in the IIoT can be accomplished by using a variety of deep learning models such as PropensityNet, deep neural network (DNN), and convolution neural networks-long short term memory network (CNS-LSTM). According to various outcomes, the suggested model is tested with Case Western Reserve University (CWRU) data. The results suggest that the method is viable and has a good level of accuracy and efficiency.

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“…28 Convolutional neural network (CNN) has a more robust capability of nonlinear feature extraction in deep learning. 29,30 Recurrent neural network (RNN) introduces memory units to make the network have a certain degree of memory. To enhance the temporal modeling capability, various methods used long short-term memory (LSTM) and gated recurrent units (GRU).…”

Section: Introductionmentioning

confidence: 99%

A novel improved data-temporal attention network reconstruction model-based method for fault diagnosis of chiller sensors

Hong

Zhao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Accurate measurement of sensors is the basis for the reliable operation of complex systems. To improve the fault diagnosis performance of the air-cooled chiller sensor, an improved data-temporal attention network is proposed, and an improved data-temporal attention network–based method for sensor fault diagnosis is established. First, the method combines the “memory” capability of the bidirectional improved long short-term memory network and the feature extraction capability of the multi-scale convolutional neural network to fully explore the time correlation of the chiller sensor, the data correlation between the physical quantities, and the dynamic response characteristics of the physical quantities. In addition, data and time attention mechanisms are introduced into the encoder and decoder, respectively, and valuable information is enhanced through weight distribution to capture relevant features further. Second, relying on the advantages of the “end-to-end” network structure of the improved data-temporal attention network model, the fault sensor is directly located by comparing the absolute reconstruction error vector with the fault threshold vector. Finally, it is verified by the datasets collected from a real air-cooled chiller platform that the proposed method has achieved an excellent fault diagnosis effect. The ablation experiment confirms that each improvement step can effectively improve the reconstruction performance of the network, which enhances the sensitivity of fault sensor identification. Compared with existing mainstream methods, the state-of-the-art framework presented in this article reveals its superiority.

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An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems

Cited by 88 publications

References 91 publications

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Fault Diagnosis Using Data Fusion with Ensemble Deep Learning Technique in IIoT

A novel improved data-temporal attention network reconstruction model-based method for fault diagnosis of chiller sensors

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