Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems

Kouadri, Abdelmalek; Hajji, Mansour; Harkat, Mohamed-Faouzi; Abodayeh, Kamaleldin; Mansouri, Majdi; Nounou, Hazem; Nounou, Mohamed

doi:10.1016/j.renene.2020.01.010

Cited by 135 publications

(59 citation statements)

References 22 publications

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“…The confusion matrix is used to compute the performance metrics of each classifier, where the classification accuracy is given the highest performance priority. Moreover, the Recall and Precision metrics are applied as per [44]:…”

Section: Simulation Resultsmentioning

confidence: 99%

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

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The operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (ML) technique for fault detection and diagnosis (FDD) of uncertain HVAC systems. The main goal of the developed MSIPCA-ML approach is to enhance the diagnosis performance, improve the indoor environment quality, and minimize the energy consumption in uncertain building systems. The model uncertainty is addressed by considering the interval-valued data representation. The performance of the proposed FDD is investigated using sets of synthetic and emulated data extracted under different operating conditions. The presented results confirm the high-efficiency of the developed technique in monitoring uncertain HVAC systems due to the high diagnosis capabilities of the interval feature-based support vector machines and k-nearest neighbors and their ability to distinguish between the different operating modes of the HVAC system.

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Section: Simulation Resultsmentioning

confidence: 99%

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

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“…Seven operating modes including one healthy and six faulty modes are used as generated simulation data series (Table 3). Each mode is adequately described over 2000 10-time-lagged samples within a 1s time period and 20 KHz sampling frequency [23]. The IGPR model is built by 2000 extracted samples.…”

Section: B Diagnosis Results and Comparison Studiesmentioning

confidence: 99%

“…The whole system is controlled to feed a fixed frequency current to the grid at unity power factor. The system parameters are presented in [23]. However, any fault in one of the above-mentioned system stages could strongly affect the power production rate [37].…”

Section: A System Descriptionmentioning

confidence: 99%

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A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

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Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.

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“…Principal component analysis (PCA) is a common feature extraction method in data science [20][21][22][23]. Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then applies those to map the data into a new subspace of equal or less dimensions [24]. In order to enhance further the quality of the feature, we proposed to use a multiscale representation with the aim of combining the ability of PCA to extract cross-correlation between variables with the ability of orthonormal wavelets to separate feature from noise and approximately decorrelate autocorrelation between available measurements.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches

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Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings and improving indoor environment quality. The main goal of this paper is to merge the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers to improve the efficiency of the detection and isolation of Air Conditioning (AC) systems. First, the multivariate statistical features extraction and selection is achieved using the PCA method. Then, the multiscale representation is applied to separate feature from noise and approximately decorrelate autocorrelation between available measurements. Third, the extracted and selected features are introduced to several machine learning classifiers for fault classification purposes. The effectiveness and higher classification accuracy of the developed Multiscale PCA (MSPCA)-based ML technique is demonstrated using two examples: synthetic data and simulated data extracted from Air Conditioning systems.

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Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems

Cited by 135 publications

References 22 publications

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches

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