Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.

Homologous fault monitoring technology of redundant INS in airborne avionics systems

Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems. A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system （RINS） in airborne navigation systems. An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertial navigation system （INS）. On this basis, the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS. The simulation is carried out based on the above algorithms, and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering. Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.

Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.

STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

Development of Magnetic Field Sensor and Motor Fault Monitoring Application

For the purpose of motor fault real-time monitoring, this research developed a nano-silicon ni- tride film based magnetic field (MF) sensor, and applied this sensor in MF detection of two common faults. Through experiment, it turned out that arc discharge and slot discharge occur in motor fault produce MF with certain laws. This result proved the feasibility of the sensor and sensing method in MF analysis, and revealed possibility of a new method in fault detection.

Design and test of fault monitoring system for corn precision planter

With the growing demand and working complexity of corn precision planter,it becomes more important to monitor the working performance through intelligent systems.A new fault monitoring system for corn precision planters was designed and tested.This system consisted of the information acquisition module,controller module,alarm module,input module and display module.A capacitive sensor was utilized to monitor the seed flow without changing the track of a precision planter.This system can monitor the whole sowing process of a seed-metering device in real-time.The sowing status,fault type and fault location can be displayed on liquid crystal display(LCD).Warning light on the LCD reminds the operator of abnormal conditions.Bench tests and field tests showed that the minimum monitoring accuracies of missing sowing and total sowing number were 92.11%and 94.28%,respectively,and the seed level sensor and the opener sensor worked well.This system can accurately prompt the seed-metering mechanism in real-time.

A new early-warning prediction system for monitoring shear force of fault plane in the active fault

The most common method used to describe earthquake activity is based on the changes in physical parameters of the earth＇s surface such as displacement of active fault and seismic wave.However,such approach is not successful in forecasting the movement behaviors of faults.In the present study,a new mechanical model of fault activity,considering the shear strength on the fault plane and the influence of the resistance force,is established based on the occurrence condition of earthquake.A remote real-time monitoring system is correspondingly developed to obtain the changes in mechanical components within fault.Taking into consideration the local geological conditions and the history of fault activity in Zhangjiakou of China,an active fault exposed in the region of Zhangjiakou is selected to be directly monitored by the real-time monitoring technique.A thorough investigation on local fault structures results in the selection of two suitable sites for monitoring potential active tectonic movements of Zhangjiakou fault.Two monitoring curves of shear strength,recorded during a monitoring period of 6 months,turn out to be steady,which indicates that the potential seismic activities hardly occur in the adjacent region in the near future.This monitoring technique can be used for early-warning prediction of the movement of active fault,and can help to further gain an insight into the interaction between fault activity and relevant mechanisms.

Activation characteristics analysis on concealed fault in the excavating coal roadway based on microseismic monitoring technique

In order to effectively monitor the concealed fault activation process in excavation activities, based on the actual condition of a working face containing faults with high outburst danger in Xin Zhuangzi mine in Huainan, China, we carried out all-side tracking and monitoring on the fault activation process and development trend in excavation activities by establishing a microseismic monitoring system. The results show that excavation activities have a rather great influence on the fault activation. With the working face approaching the fault, the fault activation builds up and the outburst danger increases; when the excavation activities finishes, the fault activation tends to be stable. The number of microseismic events are corresponding to the intensity of fault activation, and the distribution rules of microseismic events can effectively determine the fault occurrence in the mine. Microseismic monitoring technique is accurate in terms of detecting geologic tectonic activities, such as fault activations lying ahead during excavation activities. By utilizing this technique, we can determine outburst danger in excavation activities in time and accordingly take effective countermeasures to prevent and reduce the occurrence of outburst accidents.

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Early faults prediction of running state of electromechanical systems and reconfigurable integration of series safety monitoring systems

Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of modern, high-end and key electromechanical equipment, this paper will describe the early faults prediction method for multi-type electromechanical systems, which is favorable for predicting early faults of complex electromechanical systems in non-stationary, nonlinear, variable working conditions and long-time running state; the paper shall introduce the reconfigurable integration technology of series safety monitoring systems based on which the integrated development platform of series safety monitoring systems is built. This platform can adapt to integrated R&D of series safety monitoring systems characterized by high technology, multiple species and low volume. With the help of this platform, series safety monitoring systems were developed, and the Remote Network Security Monitoring Center for Facility Groups was built. Experimental research and engineering applications show that: this new fault prediction method has realized the development trend features extraction of typical electromechanical systems, multi-information fusion, intelligent information decision-making and so on, improving the processing accuracy, relevance and applicability of information; new reconfigurable integration technologies have improved the integration level and R&D efficiency of series safety monitoring systems as well as expanded the scope of application; the series safety monitoring systems developed based on reconfigurable integration platform has already played an important role in many aspects including ensuring safety operation of equipment, stabilizing product quality, optimizing running state, saving energy consumption, reducing environmental pollution, improving working conditions, carrying out scientific maintenance, advancing equipment utilization, saving maintenance charge and enhancing the level of information management.

Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

Based on the internet technology,it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine.In order to capture the micro-shock signal induced by the incipient fault on the rotating parts,the reso- nance demodulation technology is utilized in the system.As a subsystem of the remote monitoring system,the embedded data acquisi- tion instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines.Furthermore,through connecting to the internet,the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database.At the same time,the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology.Finally,the remote diagnosis software developed on the Lab VIEW platform can analyze the monitoring data from manufacturing field.The research results have indicated that the equipment status can be monitored by the system effectively.

Research on information technology of state monitoring and fault prediction for mechatronics system

The safety and reliability of mechatronics systems,particularly the high-end,large and key mechatronics equipment in service,can strongly influence on production efficiency,personnel safety,resources and environment.Based on the demands of development of modern industries and technologies such as international industry 4.0,Made-in-China 2025 and Internet + and so on,this paper started from revealing the regularity of evolution of running state of equipment and the methods of signal processing of low signal noise ratio,proposed the key information technology of state monitoring and earlyfault-warning for equipment,put forward the typical technical line and major technical content,introduced the application of the technology to realize modern predictive maintenance of equipment and introduced the development of relevant safety monitoring instruments.The technology will play an important role in ensuring the safety of equipment in service,preventing accidents and realizing scientific maintenance.

STUDY ON REALISTIC TECHNOLOGY OF CONDITION MONITORING AND FAULT DIAGNOSTIC SYSTEM FOR SHIPPING POWER DEVICES

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Study on Long-Distance Distributed Machine Condition Monitoring and Fault Diagnosis System

Based on the comparison of different machine condition monitoring and fault diagnosis systems, a distributed structure of on line condition monitoring and fault diagnosis with its implementation is put forward. A long distance distributed monitoring and diagnosis system is discussed in detail, and virtual reality (VR) with application in turbogenerator condition monitoring and fault diagnosis is also studied.

Piezomagnetic In-situ Stress Monitoring and its Application in the Longmenshan Fault Zone

The relative change of in-situ stress is an inevitable outcome of differential movement among the crust plates. Conversely, changes of in-situ stress can also lead to deformation and instability of crustal rock mass, trigger activity of faults, and induce earthquakes. Hence, monitoring real-time change of in-situ stress is of great significance. Piezomagnetic in-situ stress monitoring has good and longtime applications in large engineering constructions and geoscience study fields in China. In this paper, the new piezomagnetic in-situ stress monitoring system is introduced and it not only has overall improvements in measuring cell＇s structure and property, stressing and orienting way, but also enhances integration and intelligence of control and data transmission system, in general, which greatly promotes installing efficiency of measuring probe and quality of monitoring data. This paper also discusses the responses of new piezomagnetic system in large earthquake events of in-situ stress monitoring station at Qiaoqi of Baoxing and Wenxian of Gansu. The monitoring data reflect adjustments and changes of tectonic stress field at the southwestern segment of and the northern area near the Longmenshan fault zone, which shows that the new system has a good performance and application prospect in the geoscience field. Data of the Qiaoqi stress-monitoring station manifest that the Lushan Earthquake did not release stress of the southwestern segment of the Longmenshan fault zone adequately and there still probably exists seismic risk in this region in the future. Combined with absolute in-situ stress measurement, carrying out long-term in-situ stress monitoring in typical tectonic position of important regions is of great importance for researchers to assess and study regional crust stability.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calcu- lated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process vari- ables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of peni- cillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

Research on Early Fault Self-Recovery Monitoring of Aero-Engine Rotor System

In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method that it not only can identify the early fault of AERS but also it can do self-recovery monitoring of fault. Our method is based on the analysis of the early fault features on AERS, and it combined the SVM (Support Vector Machine) with the stochastic resonance theory and the wavelet packet decomposition and fault self-recovery. First, we zoom the early fault feature signals by using the stochastic resonance theory. Second, we extract the feature vectors of early fault using the multi-resolution analysis of the wavelet packet. Third, we input the feature vectors to a fault classifier, which can be used to identify the early fault of AERS and carry out self-recovery monitoring of fault. In this paper, features of early fault on AERS, the zoom of early fault characteristics, the extraction method of early fault characteristics, the construction of multi-fault classifier and way of fault self-recovery monitoring are studied. Results show that our method can effectively identify the early fault of AERS, especially for identifying of fault with small samples, and it can carry on self-recovery monitoring of fault.

Research on Key Techniques of Condition Monitoring and Fault Diagnosing Systems of Machine Groups

This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.

A Novel Outage Capacity Objective Function for Optimal Performance Monitoring and Predictive Fault Detection in Hybrid Free-Space Optical and RF Wireless Networks

This study develops an optimal performance monitoring metric for a hybrid free space optical and radio wireless network, the Outage Capacity Objective Function. The objective function—the dependence of hybrid channel outage capacity upon the error rate, jointly quantifies the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The objective function is developed from the basic information-theoretic capacity of the optical and radio channels using the gamma-gamma model for optical fading and Ricean statistics for the radio channel fading. A simulation is performed by using the hybrid network. The objective function is shown to provide significantly improved sensitivity to degrading performance trends and supports of proactive link failure prediction and mitigation when compared to current thresholding techniques for signal quality metrics.

An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP （ Digital Signal Processor） and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration, etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.