Real-time Fault Diagnosis System Research Articles

Research on the Application of Intelligent Diagnosis of Major Equipment Failures in Coal Mines

Abstract Currently, the implementation of a coal mine production safety monitoring system, capable of real-time monitoring of the safety condition of large coal mine equipment and timely dispatch management, is crucial for the efficient and safe operation of coal mine enterprises. This paper designs the coal mine major equipment failure process based on the rolling bearing’s vibration mechanism, analyzes the major equipment’s failure signal using both time domain and frequency domain analysis methods, and employs the intelligent fuzzy control method to identify the major equipment failure. We have designed and practically applied a real-time, multi-threaded, and comprehensive fault diagnosis and prediction system for coal mine major equipment, combining information from coal mine safety, production, and operation. The results show that the normal state’s time-domain amplitude approximates (-0.2, 0.2), and the time-domain waveforms all display periodic shock signals when the bearing sustains damage. The faulty vibration amplitude of the inner ring is within 0.08 ms 2 and the faulty vibration amplitude of the outer ring is within 0.44 ms 2, and the diagnostic accuracy using the method of this paper reaches 97.2%. This study demonstrates the effectiveness of remote monitoring and fault analysis of major equipment in coal mines, which effectively improves the equipment start-up rate and ensures coal mine production safety.

Dijagnostika kvarova zasnovana na bestežinskoj neuronskoj mreži u sistemu vešanja

Vehicle suspension systems play a critical role in ensuring passenger comfort and safety. Detecting faults in these systems is vital for maintaining safety, performance, and cost-effectiveness. Traditional inspection methods have limitations, such as visual checks, bounce tests, and alignment assessments. This study explores Wilkie, Stonham, and Aleksander Recognition Device (WiSARD), a weightless neural network (WNN), for suspension fault diagnosis. A WNN model is employed to classify suspension system faults using sensor data. The dataset includes both normal and faulty conditions to train the model. The study assesses WiSARD under various fault conditions, including strut damage, mount failure, worn-out components, and low wheel pressure. Comparative evaluations demonstrate that the approach outperforms other classification techniques, achieving an impressive 95.63% accuracy with a rapid 0.05-second computation time for test data. This WNN-based method proves superior in detecting suspension faults and holds potential as a candidate for real-time vehicle fault diagnosis systems.

Real-time fault diagnosis system for electrical panel using embedded systems

Regular maintenance inspections are necessary for industrial electrical panels. The project incorporated an IoT system that leverages embedded systems to streamline and expedite this process. This implementation rendered the electrical panel in real-time, enabling more efficient maintenance procedures. The project utilized an Odroid embedded system board as the central system controller and designed an electronic circuit for data communication. Software design was completed using the C++ programming language, and Dataplicity Cloud Commander was used as an interface for accessing the electrical panel online via phones or computers. This design allows technicians to continuously monitor the panel’s protective relays, temperatures, and power supply voltages. The system underwent testing for various operating conditions and potential errors, resulting in a low-cost intelligent system that significantly reduces troubleshooting time for electrical panels by providing essential information quickly and easily.

Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions

Advanced diagnostic algorithms and high-fidelity simulation models improve the accuracy of model-based gas path fault diagnosis for gas turbines (GTs). But simultaneously, it becomes difficult in real-time applications due to the increased calculation amount. To improve the diagnosis speed, this study adopts the surrogate method to realize the real-time gas path fault diagnosis of GTs under transient operating conditions. First, the component level model (CLM) is built and verified. Subsequently, the surrogate model is established by combining the artificial neural network (ANN) and the necessary physical model. The constructed surrogate model can almost entirely reproduce the simulation results of CLM under the whole operation conditions. Finally, the real-time fault diagnosis system combines the surrogate model and the unscented Kalman filter (UKF). The results show that the surrogate model-based fault diagnosis system has the same accuracy as the CLM-based system. At the same time, the calculation speed is increased by nearly 50 times, which meets the real-time requirements of fault diagnosis.

Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence.

Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.

Design of AGV Fault Diagnosis Data Acquisition System Based on Cloud Platform

With the development of industrial Internet of Things technology, online fault diagnosis system has become an important part of remote operation and maintenance. Aiming at the problem that the AGV online fault diagnosis system requires higher real-time data, more data volume and faster transmission rate, this paper proposes a cloud platform-based AGV fault diagnosis data acquisition system. The system uses the STM32 single-chip microcomputer to collect the sensor data carried by the AGV, uses the wireless WIFI module to upload the data to the cloud server, and finally saves the data to the cloud database after conversion and verification, which provides a test platform for the Web remote real-time monitoring and online fault diagnosis system. By building an AGV test platform to conduct experiments, the results show that the throughput rate of the system is 90PPS, and the packet loss rate is within 0.1%. The system can meet the data requirements of the online fault diagnosis system, and has good real-time performance and stability.

Towards soft real-time fault diagnosis for edge devices in industrial IoT using deep domain adaptation training strategy

Artificial intelligence and industrial internet of things (IIoT) have been rejuvenating the fault diagnosis systems in Industry 4.0 for avoiding major financial losses caused by faults in rotating machines. Meanwhile, the diagnostic systems are provided with a number of sensory inputs that introduce variations in input space which causes difficulty for the algorithms in edge devices. This issue is generally dealt with bi-view cross-domain learning approach. We propose a soft real-time fault diagnosis system for edge devices using domain adaptation training strategy. The investigation is carried out using deep learning models that can learn representations irrespective of input dimensions. A comparative analysis is performed on a publicly available dataset to evaluate the efficacy of the proposed approach which achieved accuracy of 88.08%. The experimental results show that our method using long short-term memory network achieves the best results for the bearing fault detection in an IIoT environmental setting.

A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS.

An Automated Feature Extraction Algorithm for Diagnosis of Gear Faults

Gears are used for the transfer of mechanical power and are an important part of the electromechanical transmission system. Unexpected failure of gear could cause shutdown of the machines and proves to be expensive in terms of production loss and maintenance. Therefore, reliable condition monitoring is required to protect unexpected gear failures. It has been highlighted in the recently published literature that the gear faults appear at the specific gear frequencies in the instantaneous power spectrum of the motor. However, the amplitudes of these gear frequencies are very small and are shadowed by the environment noise. Thus, reliable diagnosis of gear faults is a challenge in real-time fault diagnosis systems. This issue has been addressed in this paper through the development of the automated spectral extraction algorithm. The theoretical investigation has been verified through the custom-designed experimental test rig.

Real-time power switch fault diagnosis and fault-tolerant operation in a DFIG-based wind energy system

As wind energy production increases, reliability and fault-tolerant operation capability of wind energy systems are becoming more important. Modern wind energy production systems use power electronic converters which are unfortunately proved to be one of their most fragile parts and responsible for most of the system downtime. Real-time fault monitoring, fault diagnosis and proper system reconfiguration in these converters are therefore mandatory for fault-tolerant operation of the converter and the whole system. In this paper, a comprehensive fault-tolerant power electronics interface for wind energy systems with doubly-fed induction generators is presented and validated. The same power production capability will be guaranteed. The proposed solution also includes a robust and very fast open-circuit switch fault diagnosis algorithm, which is not sensitive to the operation conditions and is therefore suitable for this application. Simulation, Hardware in the Loop validation and experimental tests are carried out to demonstrate the effectiveness of the proposed approach.

Real-Time Fault Detection and Diagnosis System for Analog and Mixed-Signal Circuits of Acousto–Magnetic EAS Devices

The paper discusses fault diagnosis of the electronic circuit board, part of acousto–magnetic electronic article surveillance detection devices. The aim is that the end-user can run the fault diagnosis in real time using a portable FPGA-based platform so as to gain insight into the failures that have occurred.

Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors

Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.

Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network.

Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

Fault Diagnosis of HCCB TES for ITER Based on Functional Modeling

China has designed a test blanket system (TBS) based on helium cooled ceramic breeder (HCCB) test blanket module (TBM) for international thermonuclear experimental reactor (ITER). Since the system is complicated, it is so difficult to achieve the safety and stability requirement of TBS under operating condition that a real time fault diagnosis system need to be developed. This survey proposes a novel fault diagnosis method based on functional modeling, which can satisfy the safety and stability requirement of TBS. After giving a brief description of HCCB tritium extraction system (TES) that is one of the TBM’s ancillary systems, the paper presents the fault diagnosis method based on functional modeling by using multilevel flow modeling in detail. Then the TES is modeled and analyzed using this novel method. At last, a conclusion is drawn illustrating that this novel method is suitable for HCCB TBS and ITER.

Design of the Real-Time Monitoring and Fault Diagnosis System of Equipment

This paper studies on a new type design of a real-time monitoring and fault diagnosis system of equipment. The system uses subject knowledge, such as sensor technology, neural network and embedded systems. It regards system voltage and current as the monitoring goals at the same time and shows the text of the system malfunction on the liquid crystal display. The paper introduces a new system monitoring approach combined with text, sound, light alarm to achieve the goal that the process of operating the equipment is safe, stable, balanced and low loss.

Real-Time Data-Based Fault Diagnosis System

Mining from the equipment/technology process historical data can find diagnosis knowledge. Real-time analysis and evaluation on the status of equipment can be realized based on its current state parameters and historical information, which detect the potential fault rapidly and protect equipment to avoid failures further. This paper did an in-depth study in real-time data-based fault diagnosis system, built a real-time data integration platform and accomplished intelligent diagnosis method by ANFIS networks with the mined historical data. The production process was diagnosed and evaluated online with this diagnosis method. Combined with actual production system, its prototype system was developed.

Fault Detection and Isolation Based on Neural Networks Case Study: Steam Turbine

The real-time fault diagnosis system is very important for steam turbine generator set due serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using Levenberg-Marquardt algorithm related to tuning parameters of Artificial Neural Network (ANN). The model of novel fault diagnosis system by using ANN are built and analyzed. Cases of the diagnosis are simulated. The results show that the real-time fault diagnosis system is of high accuracy and quick convergence. It is also found that this model is feasible in real-time fault diagnosis. The steam turbine is used as a power generator by SONELGAZ, an Algerian company located at Cap Djinet town in Boumerdes district. We used this turbine as our main target for the purpose of this analysis. After deep investigation, while keeping our focus on the most sensitive parts within the turbine, the weakest and the strongest points of the system were identified. Those are the points mostly adequate for failure simulations and at which the designed system will be better positioned for irregularities detection during the production process.

Real-time fault diagnosis of nonlinear systems

This paper concerns the development of a real-time fault detection and diagnosis system for a class of electrical machines. Changes in the system dynamics due to a fault are detected using nonlinear models, namely, nonlinear functions of the measurable variables. At the core of the fault detection and diagnosis system are artificial neural networks and a new neural network structure designed to capture temporal information in the input data. Difficulties such as voltage unbalance, measurement noise, and variable loads, commonly found in practice, are overcome by the system addressed in this paper. Because false alarms are significantly reduced and the system is robust to parameter variations, high detection and diagnosis performance are achieved during both, learning and testing phases. Experimental results using actual data are included to show the effectiveness of the real-time fault detection system developed.

Real-time fault diagnostic system for a steam turbine generator set by using a fuzzy cerebellar model articulation controller

Because a serious fault would result in a reduced amount of electricity supply in a power plant, the real-time fault diagnosis system is extremely important for a steam turbine generator set. A novel real-time intelligent fault diagnosis system is proposed by using a fuzzy cerebellar model articulation controller (CMAC) neural network to detect and identify the faults and failures of critical components. A framework of the fault diagnosis system is described. The model of a novel fault diagnosis system by using a fuzzy CMAC is built and analysed in detail step by step. A case of the diagnosis including three faults is simulated with a fuzzy CMAC. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. Moreover, the model is verified by two examples. It is found that this model is feasible. Finally, the effects of the generalization parameter and address number in fault diagnosis are discussed.

임베디드 타입의 실시간 BLDC 전동기 고장진단 시스템 구현

본 논문에서는 주성분 분석 기법에 의한 BLDC 전동기의 고장진단 알고리즘과 임베디드 타입의 실시간 고장진단 시스템을 구현하였다. 우선 오프라인 상태에서 제안된 고장진단 알고리즘을 검증하기 위해 BLDC 고장진단 실험장치를 구현한 후 LabVIEW 프로그램에 의해 다양한 고장 데이터를 취득하였다. 취득된 데이터는 신호특성에 맞는 전 처리과정을 수행한 후 주성분분석 기법에 의해 고장특성을 나타내는 특징을 추출하고 최종적으로 BLDC 전동기의 진단은 유클리디안 거리 유사도 방법에 의해 수행된다. 이러한 결과를 바탕으로 임베디드 타입의 실시간 BLDC 고장진단 시스템을 구현하였다. 제안된 방법은 다양한 실험을 통하여 성능을 평가하였다.