Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering

Zhang, Hongtao; Chen, Huanxin; Guo, Yabin; Wang, Jiangyu; Li, Guannan; Shen, Limei

doi:10.1016/j.applthermaleng.2019.114098

Cited by 57 publications

(26 citation statements)

References 22 publications

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“…Fault Detection using ML: Many studies on vibration-related failures and predictive failure diagnosis have been conducted [3,[9][10][11][12][13][14][15][16][17][18][19][20][21]. Lee et al [3] proposed a rotating mechanism system-a mixture of feature extraction and selection classifies it as a Support Vector Machine (SVM) [4].…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al [17] proposed chiller fault detection to enable fast parameter determination without expert assistance using the Bayesian network. Zhang et al [18] proposed a clustering-based Principal Component Analysis (PCA) to propose a fault detection method for water heat pump systems. Yoo et al [19] proposed a Fault Detection method using multi-mode PCA and Gaussian mixed model in a sewage heat pump system.…”

Section: Related Workmentioning

confidence: 99%

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A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

Lee

Yang

et al. 2021

Applied Sciences

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Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

Lee

Yang

et al. 2021

Applied Sciences

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“…Furthermore, the model-based technique was being transformed into the data-driven technique by that computation power has been increasing for more than ten years [12]. There are various data-driven methods for system diagnosis, such as principal component analysis [13], support vector machine [14], nearest prototype classifier [15], k-means clustering [16] and neural network [17], etc. In last few years, several articles have been devoted to the study of system diagnosis using deep learning [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Kim

Lee

Jeon³

et al. 2020

Processes

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Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients of the discrete wavelet transform. The experimental results showed that the model which uses feature data has better performance than the model that uses only cutting force data. The feature based models are lower false negative rate (FNR) and false positive rate. Moreover, squared prediction error using normalized residual vector also reduced FNR because normalization reduces weight bias.

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“…A new and high-performance condition monitoring method based on four-stage incremental learning was proposed to solve the problems of various operation conditions of industrial camshaft machine tools [4]. Zhang et al combined subtraction clustering and k-means clustering, proposed a data-driven optimization statistical model for sensor fault detection, and applied the model to the processing of multi-operation condition data [5]. A health monitoring method of wind turbines based on supervisory control and data acquisition (SCADA) data was proposed to evaluate the health index (HI) of the wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

Shi

Zeng

2020

Symmetry

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During actual engineering, due to the influence of complex operation conditions, the data of complex systems are distinct, and the range of similarity differs under complex operation conditions. Simultaneously, the length of the data used to calculate the similarity will also impact the result of the fault detection. According to these, this paper proposes a fault detection method based on correlation analysis and improved similarity. In the first place, the complex operation conditions are divided into several simple operation conditions via the existing historical data. In the next place, the length of the data used to calculate the similarity is determined by correlation analysis. Then, an improved similarity calculation method is proposed to make the range of the similarity under multi-operation conditions identical. Finally, this method is applied to the suspension system of the maglev train. The experiment results indicate that the method proposed in this paper can not only detect the fault or abnormal state of the suspension system but also observe the health index (HI) changes of the system at distinct times under multi-operation conditions.

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Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering

Cited by 57 publications

References 22 publications

A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

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