Instrument Fault Detection Research Articles

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT.

This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist's control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments' faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations

The reliability of systems and components is a fundamental need for the efficient development of a smart distribution grid. In fact, the presence of a fault in one component of the grid could potentially lead to a service interruption and loss of profit. Since faults cannot be avoided, the introduction of a diagnostic scheme could predict the fault of a component in order to carry out predictive maintenance. In this framework, this paper proposes a novel Fault Detection and Isolation (FDI) scheme for AC/DC converters in MV/LV substations. In order to improve the reliability of the FDI procedure, the system architecture includes also an Instrument Fault Detection and Isolation section for identifying faults that could occur on the instruments and sensors involved in the monitoring process of the AC/DC converter. The proposed architecture is scalable, easily upgradable, and uses cost-effective sensors. Tests, carried out on a real test site, have demonstrated the efficacy of the proposal showing very good IFDI diagnostic performance for the 12 types of faults tested. Furthermore, as the FDI diagnostic performance regards, it shows a detection rate close to 100%.

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

Online Fault Detection of Rear Stroke Suspension Sensor in Motorcycle

This paper describes the implementation and experimental verification of an instrument fault detection (IFD) scheme for stroke sensors. In the scheme, a nonlinear autoregressive with exogenous inputs (NARX) neural network works as a soft sensor for the generation of residuals, while a rule-based decision maker provides the fault detection and classification. The scheme was thought to be implemented in the firmware of central units for the control of semiactive suspension systems for motorbikes. Execution times compatible with the real-time application constraints are reached through straightforward programing rules and suitable code optimization. These times were obtained with machine parameter values (such as clock frequency and absorbed current) far below the upper limit of the range, thanks to the adoption of a programing methodology specially designed for the real-time implementation of diagnostic schemes on general-purpose microcontrollers (MCUs). The diagnostic performance of the scheme was verified through an experimental campaign, carried out on a motorcycle featured with electronic controlled semiactive suspensions. Promptness and reliability in detecting the most common faults of the rear stroke sensor resulted fully compatible with actual applications’ expected values.

Analytically Redundancy Based IFDI Scheme for Semi-Active Suspension Systems in Motorcycle

Following the current trend of the automotive context, also in the motorcycle applications the employment of sensors for improving vehicle performance and passenger’s safety are becoming even more widespread. Among the most recent examples of this trend we can find the systems designed to electronically control and to on-line adapt the suspension subsystems. Of course, the correct operating of such systems is strictly related to the reliability of the information provided by the sensors involved in the related measurement and control chain. In this framework, the paper proposes an Instrument Fault Detection and Isolation (IFDI) Scheme for a motorcycle semi-active suspension system equipping a real prototype motorcycle. In particular, the proposed scheme is based on the analytical redundancy existing among the quantities measured by the involved sensors. As for the residuals generation, suitable soft sensors based on artificial neural networks are proposed to allow the fault detection and isolation.

NARX ANN-based instrument fault detection in motorcycle

In the context of motorcycle, we can assist to an increasing interest toward semi-active suspension control systems able to improve both the comfort and the passenger’s safety in both racing and original equipment manufacturer applications. Such systems implement suitable strategies based on the measure of several quantities, among which the relative velocity of the wheels respect to the vehicle body with the aim of regulating in real-time the damping forces. The actual effectiveness of such strategy strongly depends on the reliability and accuracy of the data measured by the sensors involved in the control loop. Due to their simplicity and good performance in terms of linearity, the most used sensors for suspension displacement measurements are based on linear potentiometers but such kind of sensors suffer of wear and tear and aging higher than the other sensors involved in the control loop strategy. As a consequence, the fault detection of such sensor is strongly recommended to avoid wrong and in some cases dangerous suspension behaviors.To this aim, in this paper a Fault Detection scheme for the rear suspension stroke sensor is designed and verified. The residual generation is based on the use of a Nonlinear Auto-Regressive with eXogenous inputs (NARX) network which is able to effectively take into account for the system nonlinearity. Experimental results have proven the good promptness and reliability of the scheme in detecting different kind of faults as “un-calibration faults” (e.g. due to slight variations of the input/output sensor curve), “hold-faults” (e.g. due to the breaking of the potentiometer cursor), “open circuit” and “short circuit” (e.g. due to electrical interruptions and short circuits, respectively).In addition, to verify the feasibility of a real-time implementation on actual processing units employed in such context, the scheme has been successfully implemented on a microcontroller STM32 based on the general-purpose ARM-M4 architecture. The validation tests and analysis have shown that the proposed Instrument Fault Detection scheme could be successfully developed on these kind of architectures by assuring a real-time operating.

A soft stroke sensor for motorcycle rear suspension

Semi-active suspension control needs to measure the relative velocity of the wheels respect to the vehicle body to regulate the damping forces. Linear potentiometers are the most used sensors in racing for linearity and simplicity, but they are expensive for mass market and unreliable in the long run. A solution to limit the cost of the vehicle electronics is the suitable exploitation of the analytical redundancy among the measured quantities of interest. The paper describes the application of a systematic approach to the optimum design of a soft displacement sensor, which may be adopted with the rear suspension of the two-wheeled vehicles. Focus is mainly devoted to the real-time exploitation of recurrent Artificial Neural Network within a typically adopted instrumentation set. Experimental results concerning with the soft sensor enable to develop an effective Instrument Fault Detection and Isolation scheme in order to improve the system reliability.

데이터 기반 경험적 모델의 원전 계측기 고장검출 민감도 평가

When an accident occurs in the nuclear power plant, the faulted information might mislead to the high possibility of aggravating the accident. At the Fukushima accident, the operators misunderstood that there was no core exposure despite in the processing of core damage, because the instrument information of the reactor water level was provided to the operators optimistically other than the actual situation. Thus, this misunderstanding actually caused to much confusions on the rapid countermeasure on the accident, and then resulted in multiplying the accident propagation. It is necessary to be equipped with the function that informs operators the status of instrument integrity in real time. If plant operators verify that the instruments are working properly during accident conditions, they are able to make a decision more safely. In this study, we have performed various tests for the fault detection sensitivity of an data-driven empirical model to review the usability of the model in the accident conditions. The test was performed by using simulation data from the compact nuclear simulator that is numerically simulated to PWR type nuclear power plant. As a result of the test, the proposed model has shown good performance for detecting the specified instrument faults during normal plant conditions. Although the instrument fault detection sensitivity during plant accident conditions is lower than that during normal condition, the data-drive empirical model can be detected an instrument fault during early stage of plant accidents.

Design of Robust Approach for Failure Detection in Dynamic Control Systems

This paper presents a robust instrument fault detection (IFD) scheme based on modified immune mechanism based evolutionary algorithm (MIMEA) that determines on line the optimal control actions, detects faults quickly in the control process, and reconfigures the controller structure. To ensure the capability of the proposed MIMEA, repeating cycles of crossover, mutation, and clonally selection are included through the sampling time. This increases the ability of the proposed algorithm to reach the global optimum performance and optimize the controller parameters through a few generations. A fault diagnosis logic system is created based on the proposed algorithm, nonlinear decision functions, and its derivatives with respect to time. Threshold limits are implied to improve the system dynamics and sensitivity of the IFD scheme to the faults. The proposed algorithm is able to reconfigure the control law safely in all the situations. The presented false alarm rates are also clearly indicated. To illustrate the performance of the proposed MIMEA, it is applied successfully to tune and optimize the controller parameters of the nonlinear nuclear power reactor such that a robust behavior is obtained. Simulation results show the effectiveness of the proposed IFD scheme based MIMEA in detecting and isolating the dynamic system faults.

Real-Time Implementation of IFDIA Scheme in Automotive Systems

The paper describes the implementation of an instrument fault detection, isolation, and accommodation (IFDIA) system developed for real-time automotive applications. The IFDIA architecture, which is based on artificial neural networks, was used to locate and accommodate faults that could occur in the main sensors involved in managing engine operation. Numerous online tests carried out in a variety of engine operating and vehicle drive conditions have confirmed the validity of the diagnostic procedure and its onboard applicability

Analytical Redundancy for Sensor Fault Isolation and Accommodation in Public Transportation Vehicles

The paper discusses an instrument fault detection, isolation, and accommodation procedure for public transportation vehicles. After a brief introduction to the topic, the rule set implementing the procedure with reference to the kinds of sensors usually installed on public transportation vehicles is widely discussed. Particular attention is paid to the description of the rules aimed at allowing the vehicle to continue working regularly even after a sensor fault develops. Finally, both the estimated diagnostic and dynamic performances in the off-line processing of the data acquired in several drive tests are then analyzed and commented upon.

함수 관측자를 이용한 고장검출식별기법에 관한 연구

Two GOS (Generalized Observer Scheme) type Fault Detection Isolation Schemes (FDIS), employing the bank of unknown input functional observers (UIFO) as a residual generator, are proposed to make the practical use of the multiple observer based FDIS. The one is IFD (Instrument Fault Detection) scheme and the other is PFD (Process Fault Detection) scheme. A design method of UIFO is suggested for robust residual generation and reducing the size of the observer bank. Several design objectives that can be achieved by the FDI schemes and the design methods to meet the objectives are described. An IFD system is constructed for the Boeing 929 Jetfoil boat system to show the effectiveness of the propositions. Major contributions of this paper are two folds. Firstly, the proposed UIFO approaches considerably reduce the size of residual generator in the GOS type FDI systems. Secondly, the FDI schemes, in addition to the basic functions of the conventional observer-based FDI schemes, can reconstruct the failed signal or give the estimates of fault magnitude that can be used for compensating fault effects. The schemes are directly applicable to the design of a fault tolerant control systems.

On-line sensor fault detection, isolation, and accommodation in automotive engines

This paper describes the hybrid solution, based on artificial neural networks (ANNs), and the production rule adopted in the realization of an instrument fault detection, isolation, and accommodation scheme for automotive applications. Details on ANN architectures and training are given together with diagnostic and dynamic performance of the scheme.

Instrument fault detection and compensation scheme for direct torque controlled induction motor drives

The effects of instrument faults in direct-torque-control-based induction motor drives are analysed and an instrument fault detection isolation scheme (IFDIS) for the drives is proposed. The IFDIS detects and isolates the incipient fault(s) of a speed sensor and current sensors in real-time. The scheme consists of an adaptive gain scheduling observer as a residual generator and a special sequential test logic unit. Although the IFDIS is a single observer scheme, it has the function of fault isolation that normally only a multiple-estimator-based IFDIS possesses. Simulation results show the detection and isolation performance of the IFDIS and the applicability of the scheme to fault tolerant control system design.

Instrument fault detection and isolation: state of the art and new research trends

This paper presents the current state-of-the-art of residual generation techniques adopted in instrument fault detection and isolation. Both traditional and innovative methods are described with their advantages and their limits. The improvement of analytical redundancy technique performances for better dealing with high-dynamics systems and/or with online applications is pointed out as the most interesting need to focus the research efforts.

An advanced neural-network-based instrument fault detection and isolation scheme

An advanced scheme for instrument fault detection and isolation is proposed. It is based on artificial neural networks (ANN's), organized in layers and handled by knowledge-based analytical redundancy relationships. ANN design and training is performed by genetic algorithms which allow ANN architecture and parameters to be easily optimized. The diagnostic performance of the proposed scheme is evaluated with reference to a measurement station for automatic testing of induction motors.

Fault Detection and Isolation with Qualitative Models

Qualitative modeling and simulation are given a more formal setting in the paper. Markov chains are used to represent qualitative behaviors generated through qualitative simulation. The filtering techniques of qualitative simulation are discussed under this setting. Based on the qualitative simulation and the observation filtering technique, the qualitative observer is constructed. The dedicated observer scheme and the generalized observer scheme are extended, to cope with the problem of instrument fault detection and isolation with qualitative models.

96/05664 Fuzzy associative memories for instrument fault detection

96/05664 Fuzzy associative memories for instrument fault detection

Fuzzy associative memories for instrument fault detection

A fuzzy logic instrument fault detection scheme is developed for systems having two or three redundant sensors. In the fuzzy logic approach the deviation between each signal pairing is computed and classified into three fuzzy sets. A rule base is created allowing the human perception of the situation to be represented mathematically. Fuzzy associative memories are then applied. Finally, a defuzzification scheme is used to find the centroid location, and hence the signal status. Real-time analyses are carried out to evaluate the instantaneous signal status as well as the long-term results for the sensor set. Instantaneous signal validation results are used to compute a best estimate for the measured state variable. The long-term sensor validation method uses a frequency fuzzy variable to determine the signal condition over a specific period. To corroborate the methodology synthetic data representing various anomalies are analyzed with both the fuzzy logic technique and the parity space approach.

A neural network approach to instrument fault detection and isolation

A neural network approach to instrument fault detection and isolation