Senior Lecturer in Offshore Renewable Energy. With expertise in marine and offshore renewable energy, I have been consistently striving to lower the Cost of Energy of offshore renewable power by developing various approaches using the knowledge in multiple disciplines. Phone: +44 191 208 6171 Address: School of Engineering Newcastle University Newcastle upon Tyne, NE1 7RU UK
Effective and efficient incipient fault diagnosis is vital to the maintenance and safe applicatio... more Effective and efficient incipient fault diagnosis is vital to the maintenance and safe application of large-scale key mechanical system. Variable predictive model–based class discrimination is a recently developed multiclass discrimination method and has been proved to be potential tool for multi-fault detection. However, the vibration signals from dynamic mechanical system always present non-normal distribution so that the original variable predictive model–based class discrimination might produce the inaccurate outcomes. An improved variable predictive model–based class discrimination method is introduced at first in this work. At the same time, variable predictive model–based class discrimination will suffer computation difficulty in the case of high-dimension input features. Therefore, a novel feature selection method based on similarity-fuzzy entropy is presented to boost the performance of the variable predictive model–based class discrimination classifier. In this method, the...
The response of a nonlinear oscillator is characterized by its instantaneous amplitude (IA) and i... more The response of a nonlinear oscillator is characterized by its instantaneous amplitude (IA) and instantaneous frequency (IF) features, which can be significantly affected by the physical properties of the system. Accordingly, the system properties could be inferred from the IA and IF of its response if both instantaneous features can be identified accurately. To fulfill such an idea, a nonlinear system parameter identification method is proposed in this paper with the aid of polynomial chirplet transform (PCT), which has been proved a powerful tool for processing nonstationary signals. First, the PCT is used to extract the instantaneous characteristics, i.e., IA and IF, from nonlinear system responses. Second, instantaneous modal parameters estimation was adopted to extract backbone and damping curves, which characterize the inherent nonlinearities of the system. Third, the physical property parameters of the system were estimated through fitting the identified average nonlinear cha...
In the past few decades, with the great progress made in the field of computer technology, non-de... more In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...]
A new mooring chain condition monitoring technique is studied in this paper by the approach of th... more A new mooring chain condition monitoring technique is studied in this paper by the approach of thermography detection. The research is conducted based on two hypotheses, i.e. (1) the streel material used to make mooring chain has much higher thermal conductivity than that of sea water. Therefore, when the mooring chain is heated, the thermal energy will transmit mostly inside the chain, rather than dispersing in water; (2) the transmission of thermal flow inside the mooring chain is disturbed by the defects. Consequently, the distribution of the temperature in the adjacent area of the defects will be different from the temperature distribution in other areas of the chains. Therefore, the discontinued distribution of the temperature may be an ideal indicator of the defect occurring in the chain. The purpose of the work reported in this paper is to demonstrate these two hypotheses using both numerical simulation and experimental methods, and thus explore an effective approach to the c...
Changes in plateau body lake water are an important indicator of global ecosystem changes, and a ... more Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (FCM) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise” of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional FCM algorithm, this paper proposes an improved FCM algorithm. This algorithm replaces the Euclidean distance of the traditional FCM algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images ...
Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliabilit... more Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliability and safety of a mechanical system. When local faults happen in a rolling bearing, the complexity of intrinsic oscillations of the vibration signals will change. Refined composite multiscale dispersion entropy (RCMDE) can quantify the complexity of time series quickly and effectively. To measure the complexity of intrinsic oscillations at different time scales, adaptive sparest narrow-band decomposition (ASNBD), as an improved adaptive sparest time frequency analysis (ASTFA), is introduced in this paper. Integrated, the ASNBD and RCMDE, a novel-fault diagnosis-model is proposed for a rolling bearing. Firstly, a vibration signal collected is decomposed into a number of intrinsic narrow-band components (INBCs) by the ASNBD to present the intrinsic modes of a vibration signal, and several relevant INBCs are prepared for feature extraction. Secondly, the RCMDE values are calculated as nonli...
Abstract Battery cells and modules are critical to the reliability of the power supply system of ... more Abstract Battery cells and modules are critical to the reliability of the power supply system of an EV. Therefore, their reliability is mainly concern before. However, EV's power supply system is a complex system. Besides battery cells and modules, it also includes many other components, the failure of which can cause a breakdown of the system as well. So, the reliability of the battery should be evaluated from the perspective of the entire power supply system rather than only considering the reliability of the battery cells and modules. This motivates the research of this paper. In the present study, the failure rates of all major components are predicted first. Then, based on the prediction results, the reliability of the entire battery system is assessed. From the assessment results, it is found that the reliability of the entire battery system is indeed much lower than those of individual components including battery modules. Moreover, as compared to the degradation of individual components, the entire system degrades more rapidly over time. Additionally, it is interestingly found that the Battery Management System (BMS) controller is not as reliable as the battery modules in the battery system considered in this paper.
Abstract Bearings are critical components in the products of many industries, and their failure c... more Abstract Bearings are critical components in the products of many industries, and their failure can result in long downtime and costly maintenance. Prolonging a bearing’s service in a safe manner is vital for operators of equipment, and condition monitoring is regarded as one of the best approaches to achieve this. In general practice, condition monitoring does successfully indicate the presence and growth of bearing faults. However, standard condition monitoring techniques do not usually have a proven method to determine the best time to conduct maintenance of a defective bearing. Traditionally, maintenance will be conducted when the measured vibration of a bearing is found to exceed an acceptable vibration level, as defined by an industry standard. However, the industry standard is only a general guideline to the design and operation of the kind of machines/components under certain conditions. The actual dynamic response of a bearing is reliant on a variety of factors, such as lubrication, loading, temperature, and operational and environmental conditions. As a consequence, although the empirical vibration levels suggested by the standard are helpful in ensuring the safe operation of a bearing, they cannot guarantee full utilization of the residual life of a defective bearing. The purpose of this paper is to try and find a feasible solution for this issue, and moreover validate it through a number of experiments conducted under various loading and operational conditions. In the research, four dimensionless condition monitoring criteria: normalized information entropy; J -Divergence; Kurtosis; and a composite criterion based on them, are employed to assess the actual health condition of the test bearings with different types and severity levels of failures. Experimental results have shown that in comparison of the industry standard, the proposed method provides an effective and feasible approach for predicting the optimum time to conduct bearing maintenance. It is deemed that the achievements of this work will help operators in further improving their management of assets.
Effective and efficient incipient fault diagnosis is vital to the maintenance and safe applicatio... more Effective and efficient incipient fault diagnosis is vital to the maintenance and safe application of large-scale key mechanical system. Variable predictive model–based class discrimination is a recently developed multiclass discrimination method and has been proved to be potential tool for multi-fault detection. However, the vibration signals from dynamic mechanical system always present non-normal distribution so that the original variable predictive model–based class discrimination might produce the inaccurate outcomes. An improved variable predictive model–based class discrimination method is introduced at first in this work. At the same time, variable predictive model–based class discrimination will suffer computation difficulty in the case of high-dimension input features. Therefore, a novel feature selection method based on similarity-fuzzy entropy is presented to boost the performance of the variable predictive model–based class discrimination classifier. In this method, the...
The response of a nonlinear oscillator is characterized by its instantaneous amplitude (IA) and i... more The response of a nonlinear oscillator is characterized by its instantaneous amplitude (IA) and instantaneous frequency (IF) features, which can be significantly affected by the physical properties of the system. Accordingly, the system properties could be inferred from the IA and IF of its response if both instantaneous features can be identified accurately. To fulfill such an idea, a nonlinear system parameter identification method is proposed in this paper with the aid of polynomial chirplet transform (PCT), which has been proved a powerful tool for processing nonstationary signals. First, the PCT is used to extract the instantaneous characteristics, i.e., IA and IF, from nonlinear system responses. Second, instantaneous modal parameters estimation was adopted to extract backbone and damping curves, which characterize the inherent nonlinearities of the system. Third, the physical property parameters of the system were estimated through fitting the identified average nonlinear cha...
In the past few decades, with the great progress made in the field of computer technology, non-de... more In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...]
A new mooring chain condition monitoring technique is studied in this paper by the approach of th... more A new mooring chain condition monitoring technique is studied in this paper by the approach of thermography detection. The research is conducted based on two hypotheses, i.e. (1) the streel material used to make mooring chain has much higher thermal conductivity than that of sea water. Therefore, when the mooring chain is heated, the thermal energy will transmit mostly inside the chain, rather than dispersing in water; (2) the transmission of thermal flow inside the mooring chain is disturbed by the defects. Consequently, the distribution of the temperature in the adjacent area of the defects will be different from the temperature distribution in other areas of the chains. Therefore, the discontinued distribution of the temperature may be an ideal indicator of the defect occurring in the chain. The purpose of the work reported in this paper is to demonstrate these two hypotheses using both numerical simulation and experimental methods, and thus explore an effective approach to the c...
Changes in plateau body lake water are an important indicator of global ecosystem changes, and a ... more Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (FCM) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise” of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional FCM algorithm, this paper proposes an improved FCM algorithm. This algorithm replaces the Euclidean distance of the traditional FCM algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images ...
Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliabilit... more Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliability and safety of a mechanical system. When local faults happen in a rolling bearing, the complexity of intrinsic oscillations of the vibration signals will change. Refined composite multiscale dispersion entropy (RCMDE) can quantify the complexity of time series quickly and effectively. To measure the complexity of intrinsic oscillations at different time scales, adaptive sparest narrow-band decomposition (ASNBD), as an improved adaptive sparest time frequency analysis (ASTFA), is introduced in this paper. Integrated, the ASNBD and RCMDE, a novel-fault diagnosis-model is proposed for a rolling bearing. Firstly, a vibration signal collected is decomposed into a number of intrinsic narrow-band components (INBCs) by the ASNBD to present the intrinsic modes of a vibration signal, and several relevant INBCs are prepared for feature extraction. Secondly, the RCMDE values are calculated as nonli...
Abstract Battery cells and modules are critical to the reliability of the power supply system of ... more Abstract Battery cells and modules are critical to the reliability of the power supply system of an EV. Therefore, their reliability is mainly concern before. However, EV's power supply system is a complex system. Besides battery cells and modules, it also includes many other components, the failure of which can cause a breakdown of the system as well. So, the reliability of the battery should be evaluated from the perspective of the entire power supply system rather than only considering the reliability of the battery cells and modules. This motivates the research of this paper. In the present study, the failure rates of all major components are predicted first. Then, based on the prediction results, the reliability of the entire battery system is assessed. From the assessment results, it is found that the reliability of the entire battery system is indeed much lower than those of individual components including battery modules. Moreover, as compared to the degradation of individual components, the entire system degrades more rapidly over time. Additionally, it is interestingly found that the Battery Management System (BMS) controller is not as reliable as the battery modules in the battery system considered in this paper.
Abstract Bearings are critical components in the products of many industries, and their failure c... more Abstract Bearings are critical components in the products of many industries, and their failure can result in long downtime and costly maintenance. Prolonging a bearing’s service in a safe manner is vital for operators of equipment, and condition monitoring is regarded as one of the best approaches to achieve this. In general practice, condition monitoring does successfully indicate the presence and growth of bearing faults. However, standard condition monitoring techniques do not usually have a proven method to determine the best time to conduct maintenance of a defective bearing. Traditionally, maintenance will be conducted when the measured vibration of a bearing is found to exceed an acceptable vibration level, as defined by an industry standard. However, the industry standard is only a general guideline to the design and operation of the kind of machines/components under certain conditions. The actual dynamic response of a bearing is reliant on a variety of factors, such as lubrication, loading, temperature, and operational and environmental conditions. As a consequence, although the empirical vibration levels suggested by the standard are helpful in ensuring the safe operation of a bearing, they cannot guarantee full utilization of the residual life of a defective bearing. The purpose of this paper is to try and find a feasible solution for this issue, and moreover validate it through a number of experiments conducted under various loading and operational conditions. In the research, four dimensionless condition monitoring criteria: normalized information entropy; J -Divergence; Kurtosis; and a composite criterion based on them, are employed to assess the actual health condition of the test bearings with different types and severity levels of failures. Experimental results have shown that in comparison of the industry standard, the proposed method provides an effective and feasible approach for predicting the optimum time to conduct bearing maintenance. It is deemed that the achievements of this work will help operators in further improving their management of assets.
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