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.

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.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

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.

CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.

GNSS receiver autonomous integrity monitoring(RAIM)algorithm based on robust estimation

Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm is only suitable for single fault detection, single GNSS constellation. However, multiple satellite failure should be considered when more than one satellite navigation system are adopted. To detect and exclude multi-fault, most current algorithms perform an iteration procedure considering all possible fault model which lead to heavy computation burden. An alternative RAIM is presented in this paper based on multiple satellite constellations（for example, GPS and Bei Dou（BDS） etc.） and robust estimation for multi-fault detection and exclusion, which can not only detect multi-failures,but also control the influences of near failure observation. Besides, the RAIM algorithm based on robust estimation is more efficient than the current RAIM algorithm for multiple constellation and multiple faults. Finally, the algorithm is tested by GPS/Bei Dou data.

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.

Modeling of reliability and performance assessment of a dissimilar redundancy actuation system with failure monitoring

Actuation system is a vital system in an aircraft, providing the force necessary to move flight control surfaces. The system has a significant influence on the overall aircraft performance and its safety. In order to further increase already high reliability and safety, Airbus has imple- mented a dissimilar redundancy actuation system （DRAS） in its aircraft. The DRAS consists of a hydraulic actuation system （HAS） and an electro-hydrostatic actuation system （EHAS）, in which the HAS utilizes a hydraulic source （HS） to move the control surface and the EHAS utilizes an elec- trical supply （ES） to provide the motion force. This paper focuses on the performance degradation processes and fault monitoring strategies of the DRAS, establishes its reliability model based on the generalized stochastic Petri nets （GSPN）, and carries out a reliability assessment considering the fault monitoring coverage rate and the false alarm rate. The results indicate that the proposed reli- ability model of the DRAS, considering the fault and redundancy degradation process and identify monitoring, can express its fault logical relation potential safety hazards.

Reliability and safety analysis of redundant vehicle management computer system

Redundant techniques are widely adopted in vehicle management computer （VMC） to ensure that VMC has high reliability and safety. At the same time, it makes VMC have special characteristics, e.g., failure correlation, event simultaneity, and failure self-recovery. Accordingly, the reliability and safety analysis to redundant VMC system （RVMCS） becomes more difficult. Aimed at the difficulties in RVMCS reliability modeling, this paper adopts generalized stochastic Petri nets to establish the reliability and safety models of RVMCS. Then this paper analyzes RVMCS oper- ating states and potential threats to flight control system. It is verified by simulation that the reli- ability of VMC is not the product of hardware reliability and software reliability, and the interactions between hardware and software faults can reduce the real reliability of VMC obviously. Furthermore, the failure undetected states and false alarming states inevitably exist in RVMCS due to the influences of limited fault monitoring coverage and false alarming probability of fault mon- itoring devices （FMD）. RVMCS operating in some failure undetected states will produce fatal threats to the safety of flight control system. RVMCS operating in some false alarming states will reduce utility of RVMCS obviously. The results abstracted in this paper can guide reliable VMC and efficient FMD designs. The methods adopted in this paper can also be used to analyze other intelligent systems＇ reliability.

Performability analysis of avionics system with multilayer HM/FM using stochastic Petri nets

The integrated modular avionics （IMA） architecture is an open standard in avionics industry, in which the number of functionalities implemented by software is greater than ever before. In the IMA architecture, the reliability of the avionics system is highly affected by the software applications. In order to enhance the fault tolerance feature with regard to software application failures, many industrial standards propose a layered health monitoring/fault management （HM/FM） scheme to periodically check the health status of software application processes and recover the malfunctioning software process whenever an error is located. In this paper, we make an analytical study of the HM/FM system for avionics application software. We use the stochastic Petri nets （SPN） to build a formal model of each component and present a method to combine the components together to form a complete system model with respect to three interlayer query strategies. We further investigate the effectiveness of these strategies in an illustrative system.