Condition Monitoring, Fault Detection and Diagnosis (FDD) of Photovoltaic System and Its Approaches

Author(s):  
Omkar Singh ◽  
Amit Kumar Yadav ◽  
Anjan Kumar Ray
Author(s):  
Xiaomeng Peng ◽  
Xiaoning Jin ◽  
Shiming Duan ◽  
Chaitanya Sankavaram

Abstract Data-driven methods for fault detection and diagnostics (FDD) require a large amount of labeled data and knowledge about complete failure modes set to train a reliable classifier as well as require the same label space in an online testing phase. Typical supervised classifiers in FDD can only predict precedented faults, limiting their performance in identifying unprecedented failure modes in on-line testing data. In addition, in most applications, it may be expensive and time-consuming to obtain sufficient labeled samples. This study focuses on fault detection and diagnosis without sufficient labels or prior knowledge of the complete set of failure modes. This paper proposes a novel FDD framework using active learning and semi-supervised learning to detect both precedented and unprecedented failures with minimum labeling effort. The effectiveness of proposed approach is demonstrated and validated using a synthetic condition monitoring dataset.


Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.


Author(s):  
Tingyu Xin ◽  
Clive Roberts ◽  
Paul Weston ◽  
Edward Stewart

Railway pantographs are used around the world for collecting electrical energy to power railway vehicles from the overhead catenary. Faults in the pantograph system degrade the quality of the contact between the pantograph and catenary and reduce the reliability of railway operations. To maintain the pantographs in a good working condition, regular inspection tasks are carried out at rolling stock depots. The current pantograph inspections, in general, are only effective for the detection of major faults, providing limited incipient fault detection or fault diagnosis capabilities. Condition monitoring of pantographs has the potential to improve pantograph performance and reduce maintenance costs. As a first step in the realisation of practical pantograph condition monitoring, a laboratory-based pantograph test rig has been developed to gain an understanding of pantograph dynamic behaviours, particularly when incipient faults are present. In the first work of this kind, dynamic response data have been acquired from a number of pantographs that have allowed fault detection and diagnosis algorithms to be developed and verified. Three tests have been developed: (i) a hysteresis test that uses different excitation speeds, (ii) a frequency response test that uses different excitation frequencies, and (iii) a novel changing gradient test. Verification tests indicate that the hysteresis test is effective in detecting and diagnosing pneumatic actuator and elbow joint faults. The frequency response test is able to monitor the overall degradation in the pantograph. The changing gradient test provides fault detection and diagnosis in the pantograph head suspension and pneumatic actuator. The test rig and fault detection and diagnosis algorithms are now being developed into a depot-based prototype together with a number of industrial partners.


Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.


2019 ◽  
Vol 107 ◽  
pp. 02001 ◽  
Author(s):  
Sayed A. Zaki ◽  
Honglu Zhu ◽  
Jianxi Yao

Among several renewable energy resources, Solar has great potential to solve the world’s energy problems. With the rapid expansion and installation of PV system worldwide, fault detection and diagnosis has become the most significant issue in order to raise the system efficiency and reduce the maintenance cost as well as repair time. This paper presented a method for monitoring, identifying, and detecting different faults in PV array. This method is built based on comparing the measured electrical parameters with its theoretical parameters in case of normal and faulty conditions of PV array. For this purpose, three ratios of open circuit voltage, current, and voltage are obtained with their associated limits in order to detect eight different faults. Moreover, the fuzzy logic control FLC method is performed for studying the failure configuration and categorizing correctly the different faults occurred. The outcomes obtained by performing the different faults representing permanent and temporary faults demonstrated that the FLC was equipped to precisely identify the faults upon their occurring. Different simulated and experimental tests are conducted to demonstrate the performance of the proposed method.


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