Fault Detection
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Dustin Helm ◽  
Markus Timusk

This work proposes a methodology for the detection of rolling-element bearing faults in quasi-parallel machinery. In the context of this work, parallel machinery is considered to be any group of identical components of a mechanical system that are linked to operate on the same duty cycle.  Quasi-parallel machinery can further be defined as two components not identical mechanically, but their operating conditions are correlated and they operate in the same environmental conditions. Furthermore, a new fault detection architecture is proposed wherein a feed-forward neural network (FFNN) is utilized to identify the relationship between signals. The proposed technique is based on the analysis of a calculated residual between feature vectors from two separate components. This technique is designed to reduce the effects of changes in the machines operating state on the condition monitoring system. When a fault detection system is monitoring multiple components in a larger system that are mechanically linked, signals and information that can be gleaned from the system can be used to reduce influences from factors that are not related to condition. The FFNN is used to identify the relationship between the feature vectors from two quasi-parallel components and eliminate the difference when no fault is present. The proposed method is tested on vibration data from two gearboxes that are connected in series. The gearboxes contain bearings operating at different speeds and gear mesh frequencies. In these conditions, a variety of rolling-element bearing faults are detected. The results indicate that improvement in fault detection accuracy can be achieved by using the additional information available from the quasi-parallel machine. The proposed method is directly compared to a typical AANN novelty detection scheme.

Fan Xiao ◽  
Jing He ◽  
Miaoying Zhang ◽  

To address the problem of demagnetization fault diagnosis of permanent magnet synchronous motor (PMSM) under inductance change, a demagnetization fault detection method based on an adaptive observer is proposed. First, the mathematical model of the demagnetization fault of PMSM in a synchronous rotating coordinate system is established, and the inductance disturbance is analyzed separately. Then, considering the different characteristics of the flux linkage fault and inductance disturbance, a new adaptive observer is proposed. Two adaptive laws are designed to ensure the accuracy of fault diagnosis and to eliminate the influence of inductance disturbance, thus achieving the robust diagnosis of demagnetization fault.

2021 ◽  
Vol 11 (18) ◽  
pp. 8734
Minki Kim ◽  
Jeongmin Yu ◽  
Nyeon-Keon Kang ◽  
Byoung-Yeop Kim

Faults represent important analytical targets for the identification of perceptual ground motions and associated seismic hazards. In particular, during oil production, important data such as the path and flow rate of fluid flows can be obtained from information on fault location and their connectivity. Seismic attributes are conventional methods used for fault detection, whereby information obtained from seismic data are analyzed using various property processing methods. The analyzed data eventually provide information on fault properties and imaging of fault surfaces. In this study, we propose an efficient workflow for fault detection and extraction of requisite information to construct a fault surface model using 3D seismic cubes. This workflow not only improves the ability to detect faults but also distinguishes the edges of a fault more clearly, even with the application of fewer attributes compared to conventional workflows. Thus, the computing time of attribute processing is reduced, and fault surface cubes are generated more rapidly. In addition, the reduction in input variables reduces the effect of the interpreter’s subjective intervention on the results. Furthermore, the clustering method can be applied to the azimuth and dip of the fault to be extracted from the complexly intertwined fault faces and subsequently imaged. The application of the proposed workflow to field data obtained from the Vincentian oil field in Australia resulted in a significant reduction in noise compared to conventional methods. It also led to clearer and continuous edge detection and extraction.

2021 ◽  
Vol 106 ◽  
pp. 110-121
Chenghong Huang ◽  
Yi Chai ◽  
Bowen Liu ◽  
Qiu Tang ◽  
Fei Qi

Ajay Kumar Shukla ◽  
Anil Kumar Kurchania ◽  

The generation of electricity through a wind turbine system is rapidly increasing. Generation of an electricity form a wind turbine is one of the preeminent renewables sources of energy as it is easily available. In many wind farms, the speed of wind is variable due to which achieving stable power output and fault detection is one of the challenges. This objective can be achieved by a doubly fed induction generator (DFIG) along with the use of a fuzzy -PID controller and two fault detection technique in WTs. This Paper shows an investigation of the fault’s detection and improvement in the DFIG model for the constant/stable power output. This model design to show DFIG 9MW (6 x 1.5) along with a 30 km transmission line and the Frequency used for RLC specification is 60 Hz. Asynchronous machine in plant of 1.68 MW, 0.93 power factor, and 2300V line voltage with mechanical power 3 x 103 W. The initial constant wind speed of 15 ms-1 is maintained. Two fault detectors, one phase fault at B25 (25 kV) before the transmission to three-phase two winding transformer. Other phase faults at B120 (120 kV) before the transmission to three phase mutual inductance. The fault actuator in the doubly fed induction generators are reliable and also maintains the safety of wind turbine connected with a grid. PID-Fuzzy Controller is introduced to regulate the speed of the rotor by adjusting pitch which controls speed changes. The result shows due to controlling of pitch angle output level is improved and a good quality factor is achieved. We have introduced a fuzzy controller so the maximum output power can be established to the grid at the trip. In this research work, mathematical modeling of DFIG is presented.

Yuqi Pang ◽  
Gang Ma ◽  
Xiaotian Xu ◽  
Xunyu Liu ◽  
Xinyuan Zhang

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through modular multilevel converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5905
Khaled Farag ◽  
Abdullah Shawier ◽  
Ayman S. Abdel-Khalik ◽  
Mohamed M. Ahmed ◽  
Shehab Ahmed

The multiphase induction motor is considered to be the promising alternative to the conventional three-phase induction motor, especially in safety-critical applications because of its inherent fault-tolerant feature. Therefore, the attention of many researchers has been paid to develop different techniques for detecting various fault types of multiphase induction motors, to securely switch the control mode of the multiphase drive system to its post-fault operation mode. Therefore, several fault detection methods have been researched and adapted; one of these methods is the indices-based fault detection technique. This technique was firstly introduced to detect open-phase fault of multiphase induction motors. The main advantage of this technique is that its mathematical formulation is straightforward and can easily be understood and implemented. In this paper, the study of the indices-based fault detection technique has been extended to test its applicability in detecting some other stator and rotor fault types of multiphase induction motors, namely, open-phase, open-switch, bad connection and broken rotor bar faults. Experimental and simulation validations of this technique are also introduced using a 1 kW prototype symmetrical six-phase induction motor.

2021 ◽  
Vol 2021 ◽  
pp. 1-19
H. A. Raeisi ◽  
S. M. Sadeghzadeh

This paper presents a new detection method of fault and partial shading condition (PSC) in a photovoltaic (PV) domestic network, considering maximum power point tracking (MPPT). The MPPT has been executed by employing a boost converter using particle swarm optimization (PSO) technique. The system is composed of two photovoltaic arrays. Each PV array contains three panels connected in series, including distinct MPPT. The PSC detection exploits the neighboring PV system data. This suggested innovative algorithm is proficient in detecting these subjects: (a) fault, (b) partial shading condition, (c) solar panel (d) panel’s relevant bypass diode failure, (d) converter failure alongside specifying the failed semiconductor, and (e) PV disconnection failure. The simulation process has been implemented using MATLAB/Simulink software. To this end, the proposed method was investigated experimentally using two 250 W PV solar set under various PSCs and faults. A data exchange link is used to implement an integrated management system. The Zigbee protocol was also chosen to data exchange of converters. The results validated the applicability and practicality of this algorithm in domestic PV systems.

2022 ◽  
Vol 202 ◽  
pp. 107555
A.F. Sartori ◽  
A.P. Morais ◽  
G. Cardoso ◽  
L.F. Freitas-Gutierres ◽  
G. Marchesan

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