Fault Monitoring System Research Articles

Fault Detection and Classification Scheme for PV System Using Array Power and Cross-Strings Differential Currents

Accurate and fast fault monitoring system is important for photovoltaic (PV) systems to reduce the damage and energy loss associated with faults. This paper presents a fast fault detection and classification scheme for PV systems. The rate of change of the measured power at the array level is firstly used to detect and differentiate between shading and PV system short-circuit faults. Then, the measured cross-strings differential currents classify the fault type in the case of short-circuit faults. The proposed method accounts for intra-string, string-to-negative terminal, cross-string (string to string), and pole-to-pole short-circuit faults, as well as open circuit faults. Two current transducers per two strings are only required to apply the method leading to a significant reduction in the number of transducers/sensors compared to the literature, making it a cost-effective solution. The fault is detected and classified in 1 ms. A 400 kW PV system consisting of 4 arrays simulated using MATLAB/Simulink package is used for the evaluation process. Short-circuit faults are imposed at different conditions of mismatch levels and fault resistance values. Also, operation at reduced irradiance level is considered, and shading faults are simulated in different levels. The simulation results prove the ability of the proposed scheme to detect and differentiate between shading and short-circuit faults. The proposed method can correctly classify different fault types in almost all cases. The low implementation cost due to the lower number of required sensors and the fast-operating time (1 ms) boost the feasibility of the proposed method.

Analysis of Piezoelectric Energy Harvesting Interface Circuit Applied to Automobile Engine Vibration

In order to realize the continuous power supply for the vibration fault monitoring system of automobile engine, aiming at the low efficiency and instability of the existing piezoelectric full bridge rectifier energy collection circuit, this paper proposes a circuit scheme based on synchronous charge extraction. The scheme can provide circuit collection efficiency, and analyze the power of the circuit by impedance analysis. Finally, the experiment shows that the theoretical analysis is consistent with the experimental results. And the synchronous electrical charge extraction circuit can harvest power up to 1.3mW under low frequency conditions, which is higher than 0.5mW collected by the full-bridge rectifier circuit under the same conditions. The harvested energy meets the power requirements of automotive sensors and microcontrollers.

Fault Location Identification in Power Transmission Networks: Using Novel Nonintrusive Fault-Monitoring Systems

This article proposes a novel fault-localization method that is based on the nonintrusive fault-monitoring (NIFM) techniques in high-voltage/extra-high-voltage (HV/EHV) power transmission networks. In this work, the fault signals measured at the utilities are extracted by the hyperbolic S-transform (HST) before going through the identification process. To carefully select coefficients of the HST representing fault transient signals and reduce the size of inputs for recognition algorithms, power-spectrum-based HST is adopted in this article to quantitatively transform the HST coefficients (HSTCs). After the processes of feature selection, the fault-location indicator is recognized by the support vector machines (SVMs). To examine and validate the proposed methodology, various fault types in a sample power system are simulated by using the electromagnetic transient program (EMTP). The simulation results reveal that the proposed methods demonstrate a high success rate of fault-location identification in power transmission networks for NIFM applications.

Detection of Vibrations Defects in Gas Transportation Plant Based on Input / Output Data Analysis: Gas Turbine Investigations

In oil and gas industrial production and transportation plants, gas turbines are considered to be the major pieces of equipment exposed to several unstable phenomena presenting a serious danger to their proper operation and to their exploitation. The main objective of this work is to improve the competitiveness performance of this type of equipment by analyses and control of the dynamic behaviors and to develop a fault monitoring system for the equipment exposed and subject to certain eventual anomalies related to the main components, namely the shaft and the rotors. This study will allow the detection and localization of vibration phenomena in the studied gas turbine based on the input / output data.

Distributed Fault Diagnosis System based on Wireless Sensor Networks

This article presents the development of a distributed fault diagnosis and monitoring system whose remote nodes are responsible for data collection and distributed analysis to identify problems that could lead to critical faults in industrial processes or systems. The developed intelligent remote node was implemented with MCU LPCXpresso54114 connected to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with HTTP communication and JSON format to the PI System industrial monitoring system database. Motor Current Signature Analysis (MCSA) was implemented and validated to identify interturn faults of induction motors. The developed platform is a tool to perform comparison and validation of analysis techniques, indicators, and fault classification, because there are different combinations that can be applied to improve diagnosis reliability, fault observability, differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, among others.

Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches

Offshore oil drilling is a complex process that requires careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring operating conditions of drilling systems are critical to the overall production cycle. In this paper, we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work, we aim to design an intelligent system for monitoring the health of IBOP system using discrete event systems (DES) based control method in combination with multivariate time series classification deep learning method. The proposed system comprises two stages: 1) produce IBOP system logical behaviour analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop an activity detection or a classifier module using reservoir computing framework for classification of multivariate time series for activity monitoring and fault detection for the top drive assembly.
 The combination of these methods would enable automation of monitoring and early detection of incidents during drilling operations. We present the preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP valve in top drive assembly and activity classification of activities relevant to IBOP condition monitoring. The effects of failure rate and repair time of each component on system performance are to be researched at a later stage.

Modeling of Three Phase Induction Motor with Dtc Drive Fault Analysis using Fuzzy Logic

Three phase induction motor drives are the most widely used drives in heavy load industries Because of its wide usage in industry, a small fault occurring in the motor drive may cause huge damage and results in failure of heavy machinaries.Inorder to avoid these failures, all the possible faults that may occur in induction motors are analysed. Based on the analysis performed, the parameters that may cause faults in the drive system are monitored. Even a minute change in the parameters are monitored using an intelligent control method named Fuzzy based monitoring system. In this monitoring system, induction motor drive is adopted with a direct torque control method to avoid the usual torque ripples present in the system. Thisintelligent fault monitoring system is used to take corrective measures within a specified time when the drive is implemented in an electric vehicle applications.

RETRACTED ARTICLE: An intelligent cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) for securing wireless sensor network (WSN)

Wireless sensor networks (WSNs) have a significant number of sensing nodes with computing and communication resources for data transmission. Many essential aspects such as computational power, storage capability, and energy consumption must be taken into account to use WSNs for data transmission. Previous research work developed a cross layer security based intrusion detection system (CLS-IDS) framework that has improved network performance, minimal energy consumption and to prolong network life, meeting QoS requirements. However, this CLS-IDS method cannot detect the faults happening at various places at the same time. So, this research work proposes the cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) and also least overhead monitoring for WSNs by means of memory and energy demands to resolve the above issue. This cross-layer security was designed with the aid of a fuzzy logic-based calculation method for a protocol called cross-layer protocol, uses multiple parameters extracted by the exchange of inter-layer information in order to alleviate the effects of security hazards to the network of wireless sensors. In order to identify malicious nodes on the network, the fault monitoring system is carried out using enhanced convolutional neural network (ECNN) classifier and the confidential value is determined for the trust values. The proposed cross layer security based fuzzy trust mechanism (CLS-FLTCM) is highly efficient technique to ensure optimum safety in wireless sensor environment when compared to the previous techniques. The simulation results conducted using the NS-2 simulator and testing the safety performance of the proposed system demonstrates that this approach is better than the current detection precision techniques.

Application and Research of Machine Learning Simulation Technology in Equipment Fault Monitoring under Smart Grid

Smart grid is increasingly becoming the development trend of grid technology. Smart grid is an important part of smart grid. Power equipment is the core part of smart grid, and the normal or not of power equipment directly affects the safety and stability of the entire power system. Smart smart grid usually has relatively complete fault diagnosis and self-healing functions to improve the stability and reliability of the grid. The application and development of a practical fault monitoring and early warning system for power equipment is to carry out predictive maintenance of power equipment so that the equipment can operate more safely and reliably. Due to the time-varying nonlinearity, stochastic uncertainty and local observability of smart grid, it is difficult for traditional power system modeling and analysis methods to fully reflect the steady- state and transient characteristics of power system in the new form. In this paper, based on machine learning simulation technology, through the research of power equipment fault monitoring system in smart grid, provide early warning information and solutions for equipment management personnel maintenance.

Low-cost and Efficient Fault Detection and Protection System for Distribution Transformer

Distribution transformers are a vital component of electrical power transmission and distribution system. Frequent Monitoring transformers faults before it occurs can help prevent transformer faults which are expensive to repair and result in a loss of energy and services. The present method of the routine manual check of transformer parameters by the electricity board has proven to be less effective. This research aims to develop a low-cost protection system for the distribution transformer making it safer with improved reliability of service to the users. Therefore, this research work investigated transformer fault types and developed a microcontroller-based system for transformer fault detection and protection system using GSM (the Global System of Mobile Communication) technology for fault reporting. The developed prototype system was tested using voltage, current and temperature, which gave a threshold voltage higher than 220 volts to be overvoltage, a load higher than 200 watts to be overload and temperature greater than 39 degrees Celsius to be over temperature was measured. From the results, there was timely detection of transformer faults of the system, the transformer protection circuits were fully functional, and fault reporting was achieved using the GSM device. Overall, 99% accuracy was achieved. The system can thus be recommended for use by the Electricity Distribution Companies to protect distribution transformers for optimal performance, as the developed system makes the transformers more robust, and intelligent. Hence, a real-time distribution transformer fault monitoring and prevention system is achieved and the cost of transformer maintenance is reduced to an extent.

Fault Monitoring System for the Multistage Gear Box Based on MSTFT and GFLA Algorithm

Gearbox functions as a significant transmission module in the mechanical devices in spite of its failure prone nature and hence there exists a need for the diagnosing the gearbox faults with an optimized solution and new methods should be engaged for improving the effectiveness, accuracy, reliability and few other such parameters. These attempts could meet the growing requirements for the condition monitoring in the detection of gear faults. The feature selection process is a notable process in the machine learning to achieve good performance in the diagnostic process. This framework builds up an innovative structure for fault diagnosis in the gearbox system. Six vibrating signals which are healthy gear signal, fault gear signal, healthy gear signal affected by noise, faulty gear signal affected by noise, distributed fault signal, and local fault signal have been generated with the prescribed dataset and the feature extraction was done by employing the Modified Short-Time Fourier Transform on the basis of Blackman window. This step was followed by selection of the features by using the combination of Genetic algorithm and Frog Leaping algorithm that is termed as GFLA.This novelty approach enhances the initialization process thereby enabling for the calculation of accurate best score calculation. The classification method is performed Support points on the basis of neural network. Finally the performance analysis with respect to the existing methodologies proved the efficiency in detecting the fault of the proposed framework.

Research on the Fault Characteristic of Wind Turbine Generator System Considering the Spatiotemporal Distribution of the Actual Wind Speed

A reliable fault monitoring system is one of the conditions that must be considered in the design of large wind farms today. The most important factor for the fault monitoring should be the accurate diagnosis criteria with sensitive fault characteristics. Most of the current fault diagnosis criteria are obtained based on the average wind speed at the center of the hub which is not in accord with the actual wind condition in nature. So, this paper utilizes an equivalent wind speed (EWS), which can describe the actual wind speed spatiotemporal distribution on the rotor disk area considering the effects of wind shear and tower shadow, to analyze the common mechanical and electrical faults again. Firstly, the EWS model applicable to the 3-blade wind turbines is introduced; then the new fault characteristics of the wind turbine rotor aerodynamic imbalance and the stator winding asymmetry are theoretically analyzed based on the EWS model; finally, the simulation platform is built in Matlab/Simulink for comparison and the simulation result is well consistent with the theory analysis. The aim of this research is to find more accurate fault characteristics and help promoting the healthy development of wind power industry.

Online fault monitoring based on deep neural network & sliding window technique

Nuclear power plants have proved their worth in energy sector by providing clean and uninterrupted power over decades. However, a Nuclear Power Plant (NPP) is a complex, dynamic system with potential radioactive release risk which makes it crucial to achieve highest standards of safety. Specially, in preview of massive monitoring data received in modern NPPs which makes it difficult for operators to extract vital information about actual plant state in a timely and accurate manner. On the other hand, advancements in latest machine learning methods have made it possible to process such massive data for operators to act accordingly. However, current machine learning approaches cited for this field, fall short of required capabilities needed for such safety critical industry. In manuscript, an online fault monitoring system is proposed which utilizes deep neural networks and sliding window technique. The proposed model not only fulfills the requirement of validity but also encompass all necessary diagnosis functions like detection, identification, assessment and robustness. The model allows for a fault to be identified and assessed in different plant states and then validate the predicted results through online correlation of simulation vs original data. The study was conducted for IP-200 NPP utilizing RELAP5 thermal-hydraulic code. The proposed model was verified by inducing 04 different faults for different states and severities. The results were found to be conducive for improving reliability and accuracy of next generation fault monitoring systems of Nuclear Power Plants.

Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0

Abstract: New online fault monitoring and alarm systems, with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT), are examined in this article within the context of Industry 4.0. The data collected from machines is used to implement maintenance strategies based on the diagnosis and prognosis of the machines' performance. As such, the purpose of this paper is to propose a Cloud Computing Platform containing three layers of technologies forming a Cyber-Physical System which receives unlabelled data to generate an interpreted online decision for the local team, as well as collecting historical data to improve the analyzer. The proposed troubleshooter is tested using unlabelled experimental data sets of rolling element bearing. Finally, the current and future Fault Diagnosis Systems and Cloud Technologies applications in the maintenance field are discussed.

Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems

Fault monitoring systems in Induction Motors (IMs) are in high demand since many production environments require yielding detection tools independent of their power supply. When IMs are inverter-fed, they become more complicated to diagnose via spectral techniques because those are susceptible to produce false positives. This paper proposes an innovative and reliable methodology to ease the monitoring and fault diagnosis of IMs. It employs fractional Gaussian windows determined from Caputo operators to stand out from spectral harmonic trajectories. This methodology was implemented and simulated to process real signals from an induction motor, in both healthy and faulty conditions. Results show that the proposed technique outperforms several traditional approaches by getting the clearest and most useful patterns for feature extraction purposes.

An H-shaped two-dimensional piezoelectric vibration energy harvester

An H-shaped piezoelectric vibration energy harvester (VEH) with two dimensional receiving direction is designed to provide power for engine fault monitoring in this work. The VEH consist of one main-beam which receives the vibrational excitation of horizontal direction, and two sub-beams which receive the vibrational excitation of vertical direction. The main-beam and the sub-beam of this H-shaped VEH are almost independent. So this H-shaped VEH could be modelled as a lumped-mass model. The models of the output voltage and the output power are given. The H-shaped VEH is fabricated using computer numerical control technology. The measured results show that the main-beam’s output voltage is 1011 mV with the resonant frequency of 110 Hz, and the sub-beam’s output voltage is 1021 mV with the resonant frequency of 118 Hz. The measured maximum output power of main-beam is 24.5 μW when the sliding rheostat is at 12.9 kΩ. And the measured maximum output power of sub-beam is 12.3 μW when the sliding rheostat is at 29.0 kΩ. The power density of the main-beam is about 0.286 μW mm−3, and the power density of the sub-beam is about 0.149 μW mm−3. Therefore, this H-shaped VEH can provide an effective solution of energy harvesting for engine fault monitoring system. In the future, the size of the structure will be reduced by MEMS technology and this H-shaped VEH can be integrated with other electronic devices.

Design and Research of Overhead Line Fault Monitoring System for Medium Voltage Distribution Network Based on LoRa and NB-IoT

Based on the analysis of the characteristics of LoRa and NB-IoT technology, a data transmission scheme of hybrid 10KV overhead line fault monitoring system based on LoRa and NB-IoT is proposed to solve the problems caused by GPRS in long-distance data transmission of existing overhead line fault monitoring system of medium-voltage distribution network. This paper introduces its network frame structure, designs corresponding hardware terminal equipment and software program, and finally tests and analyzes the system. The experimental results show that the scheme has the characteristics of long communication distance, low power consumption, flexible networking and high reliability, and has a wide application prospect.

A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

This paper presents a contribution to the fault monitoring approach and input–output feedback linearization control of the induction motor (IM) in the closed-loop drive. Two kinds of faults are considered in the machine, particularly the broken rotor bars and stator inter-turn short circuit faults. This approach has been employed to detect and identify simple and mixed defects during motor operation by utilizing advanced techniques. To achieve it, two procedures are applied for the fault monitoring: The model-based strategy, which used to generate a residual speed signal to indicate the presence of possible failures, by means the high gain observer in the closed-loop drive. However, this strategy is not able to recognise the type of faults but it can be affected by the disturbances. Therefore, the neural network (NN) technique is applied in order to identify the faults and distinguish them. However, the NN required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform and fast Fourier transform is applied to extract the amplitude of the harmonics and used them as an input data set for NN. The obtained results show the efficiency of the fault monitoring system and its ability to detect and diagnosis any minor faults in a closed loop of the IM.

Online fault detection system for proportional hydraulic valves

This paper presents the development of a method for condition monitoring and online fault detection on proportional directional valves. The systems that such valves are part of might be sensitive for unexpected maintenance or long duration stops. Consequently, the implementation of a fault detection and monitoring system can reduce maintenance costs and increase safety. The method is based on monitoring both the valve supply current and spool position related to the spool positioning control signal. Therefore, it is applicable for valves with embedded electronics including spool position measurement and internal controller. The supply current and spool position behavior depends on the friction, flow forces, solenoid current, and valve closed loop controller performance. Furthermore, valve static and dynamic characteristics are influenced by the spool size, overlapping and manufacturing tolerances. The effectiveness of the method to monitor and detect faults in valves with different sizes and constructive parameters is shown experimentally using five different proportional valves. The proposed method requires reference parameters characterizing the valve operation without faults. Standard tests are proposed to determine healthy valve parameters. For the method valuation and validation, experimental results with the valve operating under healthy conditions and with induced faults were compared. Faults were added in a way to represent spool locking and increase of friction forces between the spool and sleeve. The obtained results show the capability the method for the detection of faults classified as severe even if the valve controller attempts to compensate the faulty behavior.

A MEMS based piezoelectric vibration energy harvester for fault monitoring system

Normally, the ambient vibrations demonstrates low acceleration amplitude (< 1 g) and low frequency (60–300 Hz), so most researchers focus on the study of the low frequency vibration energy harvester at present. However, the power output of the low frequency vibration energy harvester is low and thus difficult to meet the application requirement. In this paper, a MEMS based piezoelectric vibration energy harvester was designed and fabricated for the application of the fault monitoring system for ship engine which vibrates at high acceleration amplitude and high frequency. Firstly, the vibration characteristics of the ship engine was tested. Under normal operating conditions, the vibration frequency and acceleration of the ship engine are 600–700 Hz and 2–4 g, respectively. And then a MEMS based high frequency piezoelectric vibration energy harvester was designed under the vibration environment. Finally, the designed harvester was fabricated and evaluated. The voltage and power output of the fabricated harvester is 5.81 V and 660.1 μW, which meet the application requirements of the fault monitoring system.