Gradual Faults Research Articles

A laboratory perspective on accelerating preparatory processes before earthquakes and implications for foreshock detectability

Dynamic failure in the laboratory is commonly preceded by many foreshocks which accompany premonitory aseismic slip. Aseismic slip is also thought to govern earthquake nucleation in nature, yet, foreshocks are rare. Here, we examine how heterogeneity due to different roughness, damage and pore pressures affects premonitory slip and acoustic emission characteristics. High fluid pressures increase stiffness and reduce heterogeneity which promotes more rapid slip acceleration and shorter precursory periods, similar to the effect of low geometric heterogeneity on smooth faults. The associated acoustic emission activity in low-heterogeneity samples becomes increasingly dominated by earthquake-like double-couple focal mechanisms. The similarity of fluid pressure increase and roughness reduction suggests that increased stress and geometric homogeneity may substantially shorten the duration of foreshock activity. Gradual fault activation and extended foreshock activity is more likely observable on immature faults at shallow depth.

Research on automatic detection of gradual fault of high voltage electric energy metering transformer based on fuzzy rough set and whale optimization algorithm

Based on fuzzy rough set and whale optimization algorithm, the automatic fault detection method of high-voltage electric energy metering transformer is studied to improve the fault diagnosis effect and efficiency. On the basis of constructing the mathematical model of gradual fault of high-voltage electric energy metering transformer, the fuzzy rough set theory is used to reduce the data attributes of fault samples, eliminate similar attributes, determine the minimum fault feature set, and complete the fault feature selection, which is used as the input of the fault detection model based on Whale Optimization Algorithm-based Support Vector Machine (WOA-SVM). After the kernel parameters and penalty factors of SVM are optimized by whale optimization algorithm, the type of gradual fault of high-voltage electric energy metering transformer is identified. The experimental results show that the reduced fault attributes are distributed differently in the sample data, and the fault detection accuracy can be improved by 9.5 % through fault feature selection. The fault diagnosis model with Gaussian radial basis function, kernel parameter of 0.05 and penalty factor of 10 has the best performance. This method can identify the gradual fault types of high-voltage electric energy metering transformers, and the fault diagnosis effect is outstanding.

Study of the fault diagnosis method for gas turbine sensors based on inter-parameter coupling information

Correct and reliable measurement data are crucial for state monitoring, safe operations, health assessment, and life prediction of integrated energy systems (IESs). Sensors are often installed in harsh environments and prone to all kinds of faults; therefore, it is necessary to diagnose sensor faults. A diagnostic method for sensor faults based on gradient histogram distribution (GHD) combined with light gradient boosting machine (LightGBM) is presented in this paper. This proposed method effectively utilizes the coupling information between the relevant parameters. The GHD efficiently extracted the time-domain characteristics of sensor faults and reduced the dimension of eigenvectors. This is beneficial to increasing the diagnostic speed. The kernel density estimation distributions of the gradient and eigenvectors for the sensor with strong correlation are similar, but that for the sensor with weak correlation are completely different. A LightGBM classifier trained based on the feature vectors was utilized to diagnose and classify the sensor faults. The diagnosis accuracy and the diagnosis time of this developed method were examined using the multiple-condition practical operation data of gas turbines in the IES. The experiment results demonstrate that the diagnostic accuracy of five sensor faults using this developed method is all above 90%. The diagnostic time is about 0.47–1.34 s, and is less than 2 s for the gradual faults.

Fault Mode Analysis and Convex Optimization-Based Fault-Tolerant Control for New Type Dissimilar Redundant Actuation System of Near Space Vehicle

A new type dissimilar redundant actuation system (NT-DRAS), which is composed of an electro-hydrostatic actuator (EHA) and an electro-mechanical actuator (EMA), is applied in high value unmanned aerial vehicles such as the future near space vehicles to improve their reliability and performance index simultaneously. Further improvement in the flight safety is achieved with the fault-tolerant control (FTC) technique which deals with system faults. This paper proposes a novel convex optimization-based fault-tolerant control (CO-FTC) strategy for the NT-DRAS subject to gradual faults which are included in the state space representation of the system. A convex analysis-based treatment for system uncertainty caused by gradual faults is applied to determine the control gain matrix. The existence condition of the control gain matrix is optimized in the linear matrix inequality (LMI) form. Finally, the determined subsystems based on the novel technique is used to solve the modeled robust FTC problem. Case studies of NT-DRAS subject to different gradual faults have been accomplished to illustrate the FTC necessity for NT-DRAS. Furthermore, the effectiveness of the proposed CO-FTC strategy is validated by comparative analysis of the simulation results.

Cooperative Positioning Method of a Multi-UAV Based on an Adaptive Fault-Tolerant Federated Filter.

Aiming at the problem of the low cooperative positioning accuracy and robustness of multi-UAV formation, a cooperative positioning method of a multi-UAV based on an adaptive fault-tolerant federated filter is proposed. Combined with the position of the follower UAV and leader UAV, and the relative range between them, a cooperative positioning model of the follower UAV is established. On this basis, an adaptive fault-tolerant federated filter is designed. Fault detection and isolation technology are added to improve the positioning accuracy of the follower UAV and the fault tolerance performance of the filter. Meanwhile, the measurement noise matrix is adjusted by the adaptive information allocation coefficient to reduce the impact of undetected fault information on the sub-filter and global estimation accuracy. The simulation results show that the adaptive fault-tolerant federated algorithm can greatly improve the positioning accuracy, which is 83.4% higher than that of the absolute positioning accuracy of a single UAV. In the case of a gradual fault, the method has a stronger fault-tolerant performance and reconstruction performance.

A generalized gradual fault diagnosis using photovoltaic plant reflectometry profile

This paper presents a generalized gradual fault diagnosis technique for photovoltaic (PV) plants using photovoltaic plant reflectometry profile (PPRP). PV plants can suffer from many types of defects; the most critical are the electrical ones that may appear either after a long or a short period of time. Many fault detection techniques are used depending on fault types, and reflectometry is one of the most efficient. Nevertheless, it has its limitations, such as location detection of the first fault only within a PV string, in addition to not being able to identify the fault type. This paper presents a mathematical model of the reflectometry technique, named hereafter the photovoltaic plant reflectometry profile (PPRP), that enhances its performance by enabling it to detect and locate multiple faults on a N-PV string, in addition to detecting any impedance mismatches, even the smallest ones, between a good PV string and one with faulty modules. Simulations were conducted to evaluate the accuracy of the PPRP analytical model considering a 7-PV string and its effectiveness to detect the expected locations of the faulty modules, and results show that the simulated reflected signal matches exactly the expected PPRP using the developed mathematical model.

Design and Verification of Simulation Data Generator for Rail Transit Switch Machine

Because the fault data of rail transit switch machine are difficult to obtain and the site fault is difficult to reproduce, it is difficult to diagnose or predict the switch machine. In this paper, the power fault data of S700K switch machine is divided into creeping fault and mutation fault, and a simulation data generator for generating massive fault data is developed. The generators involve the synthesis of minority over-sampling techniques and generative confrontation networks. Finally, the long-term memory neural network is used to predict the generated gradual fault data to verify the authenticity and reliability of the simulation data generator. The experimental results show that the generated data can predict the future power trend of the switch machine, which proves the authenticity and feasibility of the simulation data generator.

A Fault-Tolerant Scheme Based on MSVR for Integrated Navigation

To enhance the reliability of integrated navigation systems, an intelligent fault-tolerant scheme based on multi-output support vector regression (MSVR) is proposed. Specifically, a novel fault detection function that is made up of the filtering innovations and the estimated ones is designed so that it can timely detect the faults that may occur. Meanwhile, MSVR is utilized to build the estimation model for more reliable estimations. The special construction of the fault detection function makes it very sensitive to faults. Additionally, a variable that symbolizes the confidence of measurement is defined and applied to realize fault isolation. The method effectively improves the navigation accuracy at the initial stage of the occurrence of a gradual fault. The results of experiments reveal that the proposed algorithm has a stronger fault-tolerant ability compared with the traditional algorithm. Concretely, when dealing with the artificial faults, the position precision in north and east directions are increased up to 70.06% and 67.78%, respectively, by applying the proposed fault-tolerant scheme, in comparison with the residual chi-square test.

GAN-FDSR: GAN-Based Fault Detection and System Reconfiguration Method

Fault detection and exclusion are essential to ensure the integrity and reliability of the tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integrated navigation system. A fault detection and system reconfiguration scheme based on generative adversarial networks (GAN-FDSR) for tightly coupled systems is proposed in this paper. The chaotic characteristics of pseudo-range data are analyzed, and the raw data are reconstructed in phase space to improve the learning ability of the models for non-linearity. The trained model is used to calculate generation and discrimination scores to construct fault detection functions and detection thresholds while retaining the generated data for subsequent system reconfiguration. The influence of satellites on positioning accuracy of the system under different environments is discussed, and the system reconfiguration scheme is dynamically selected by calculating the relative differential precision of positioning (RDPOP) of the faulty satellites. Simulation experiments are conducted using the field test data to assess fault detection performance and positioning accuracy. The results show that the proposed method greatly improves the detection sensitivity of the system for small-amplitude faults and gradual faults, and effectively reduces the positioning error during faults.

Fault Diagnosis Method and Application of ESP Well Based on SPC Rules and Real-Time Data Fusion

Aiming at the popularization and application of a real-time monitoring parameter acquisition system of the electric submersible pump (ESP) well, this paper proposes a fault diagnosis method of ESP well operation based on the SPC rule and prior knowledge fusion. Based on the study of parameter variation rules of ESP well, the SPC expansion rule model is established; by analyzing the variation of some typical characteristic parameters of ESP well, combined with SPC expansion rules and expert experience, a priori knowledge of fault diagnosis of ESP well is formed, that is, multiparameter fault analysis table and weight factor; the SPC extended rule model and prior knowledge are fused to establish the fault probability model of ESP well, form the fault diagnosis method of ESP well, develop the online fault diagnosis software of ESP well, and deploy it in 425 ESP wells in a block. Taking five types of tubing leakage, pump wear, shaft breakage, gas influence, and pump plugging as examples, the application process of fault diagnosis method is analyzed. The research and application show that compared with other fault diagnosis methods, this method needs a smaller time window and higher diagnosis accuracy. By setting multiple time windows, this diagnosis method is applied to calculate the fault probability of ESP well in real time, solve the real time and accurate identification of 14 sudden faults and gradual faults, and significantly improve the intelligent diagnosis level of production faults of ESP well.

Fault Detection and Diagnosis for Plasticizing Process of Single-Base Gun Propellant Using Mutual Information Weighted MPCA under Limited Batch Samples Modelling

Aiming at the lack of reliable gradual fault detection and abnormal condition alarm and evaluation ability in the plasticizing process of single-base gun propellant, a fault detection and diagnosis method based on normalized mutual information weighted multiway principal component analysis (NMI-WMPCA) under limited batch samples modelling was proposed. In this method, the differences of coupling correlation among multi-dimensional process variables and the coupling characteristics of linear and nonlinear relationships in the process are considered. NMI-WMPCA utilizes the generalization ability of a multi-model to establish an accurate fault detection model in limited batch samples, and adopts fault diagnosis methods based on a multi-model SPE statistic contribution plot to identify the fault source. The experimental results demonstrate that the proposed method is effective, which can realize the rapid detection and diagnosis of multiple faults in the plasticizing process.

A novel entropy-based fault diagnosis and inconsistency evaluation approach for lithium-ion battery energy storage systems

Detection and diagnosis of faults at the early stage, as well as inconsistency monitoring and control are of extreme importance for operating Li-ion batteries (LIBs) safely and reliably, handling performance degradation and cell unbalancing, and avoiding accidents like thermal runaway (TR). In this work, a general procedure based on multi- level Shannon entropy algorithms is put forward to perform fault diagnosis as well as inconsistency evaluation for LIB-based energy storage systems (ESSs). More specifically, the cell-level Shannon entropy algorithm is used to detect faults by comparing Shannon entropies of different LIB cells in each module while the module-level and cluster-level Shannon entropy algorithms are used to evaluate the overall inconsistency among LIB cells in each module and in each cluster respectively. The proposed approach is then applied in a large-scale LIB-based ESS (1 MW/2 MWh). Through simulated data, the availability of the cell-level Shannon entropy algorithm to detect small changes in gradual faults is testified while the module-level and the cluster-level Shannon entropy algorithms are demonstrated to be effective for assessing inconsistences of LIBs in every module and in every cluster respectively, by comparing results of the normal case with those from two cases each with a different faulty LIB cell at the early stage of internal short circuit (ISC).

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine life-cycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry.

A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics

The removal of noise from signals obtained through the health monitoring systems in gas turbines is an important consideration for accurate prognostics. Several filters have been designed and tested for this purpose, and their performance analysis has been conducted. Linear filters are inefficient in the removal of outliers and noise because they cause smoothening of the sharp features in the signal which can indicate the onset of a fault event. On the other hand, non-linear filters based on image processing methods can provide more precise results for gas turbine health signals. Among others, the weighted recursive median (WRM) filter has been shown to provide greater accuracy due to its weight adaptability depending on the signal type. However, sampling data at high rates is possible which needs hardware implementation of the filter. In this paper, the design, simulation and implementation of WRM filters on the FPGA (Field Programmable Gate Arrays) platforms Vivado Design Suite by Xilinx and Quartus Pro Lite Edition 19.3 has been performed. The architectural detail and performance result with the FPGA filters when subjected to abrupt and gradual fault signal is presented.

Symbiotic System of Systems Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farms

To reduce Operation and Maintenance (O&M) expenditure on offshore wind farms, wherein 80% of the cost relates to deploying personnel, the offshore wind sector looks to advances in Robotics and Artificial Intelligence (RAI) for solutions. Barriers to residential Beyond Visual Line of Sight (BVLOS) autonomy as a service, include operational challenges in run-time safety compliance, reliability and resilience, due to the complexities of dealing with known and unknown risk in dynamic environments. In this paper we incorporate a Symbiotic System Of Systems Approach (SSOSA) that uses a Symbiotic Digital Architecture (SDA) to provide a cyber physical orchestration of enabling technologies. Implementing a SSOSA enables Cooperation, Collaboration and Corroboration (C3), as to address run-time verification of safety, reliability and resilience during autonomous missions. Our SDA provides a means to synchronize distributed digital models of the robot, environment and infrastructure. Through the coordinated bidirectional communication network of the SDA, the remote human operator has improved visibility and understanding of the mission profile. We evaluate our SSOSA in an asset inspection mission within a confined operating environment. Demonstrating the ability of our SSOSA to overcome safety, reliability and resilience challenges. The SDA supports lifecycle learning and co-evolution with knowledge sharing across the interconnected systems. Our results evaluate both sudden and gradual faults, as well as unknown events, that may jeopardize an autonomous mission. Using distributed and coordinated decision making, the SSOSA enhances the analysis of the mission status, which includes diagnostics of critical sub-systems within the resident robot. This evaluation demonstrates that the SSOSA provides enhanced run-time operational resilience and safety compliance to BVLOS autonomous missions. The SSOSA has the potential to be a highly transferable methodology to other mission scenarios and technologies, providing a pathway to implementing scalable autonomy as a service.

Research on battery pack fault pre diagnosis method

At present, most of the BMS in the market set a constant fault threshold, but with the increase of the number of power battery recycling, the external characteristics of the battery will also change. A constant fault diagnosis threshold may also occur after battery aging diagnosis is not timely. Battery cell voltage is the direct embodiment of the external characteristics of battery cell, so the fault diagnosis of battery cell voltage is the most important. Too high or too low cell voltage is the most obvious fault. Normally, this should not happen. Of course, due to the gradual aging of the battery pack, the inconsistency between the monomers and the charged state of each cell, the inconsistency of the voltage of each cell can not be avoided. However, the voltage difference caused by cell manufacture or inconsistency is limited in the case of aging degree.Based on the battery discharge data, the purpose of this study is to propose a method to identify the gradual fault signal in advance and reduce the problems caused by the sudden failure of the battery by detecting and diagnosing the change trend of the extreme value difference of the historical operation data.

Ensemble learning with member optimization for fault diagnosis of a building energy system

For better service and energy savings, improved fault detection and diagnosis (FDD) of building energy systems is of great importance. To achieve this aim, ensemble learning is investigated and introduced in this study. Three types of methods like K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) are carefully selected, optimized, and integrated into an ensemble diagnostic model (EDM) by the majority voting method. Experimental data for seven typical gradual faults in a centrifugal building chiller are used for model validation and evaluation. The results show that the diagnostic accuracy of the EDM (99.88%) is higher than that of the individual methods, with significant improvements for normal operation and refrigerant leakage, and no false alarms reported. Models based on ensemble learning, EDM and RF (homogenous ensemble), exhibit better performance for global faults, which are difficult to diagnose. In addition, five different feature sets are selected from the literature for further tests. It is found that the diagnostic performance depends not only on the principle of diagnosis, but also on the fault category and the characteristics of the feature set such as the indicative degree to corresponding faults, number of features, correlation degree between features, and information redundancy, and feature selection is proved to be more important than algorithm selection in fault diagnosis practice. Ensemble learning is proved to be a promising candidate for the fault diagnosis of building energy systems, except for the Zhou-8 feature set (eight temperature features), for which KNN is a better choice.

Fault detection in ventilation units using dynamic energy performance models

Buildings systems are one of the world's largest energy consumer, and their faults often cause energy waste and occupants discomfort. In this paper, we propose a model-based method for fault detection and diagnostics of ventilation units supported by a set of rules. Under-performing subsystems are first isolated by comparing the expected energy consumption obtained through a dynamic energy model, then the faulty component is diagnosed using a set of rules. The method is tested on the EnergyPlus model of an existing building. Two types of faults are introduced by changing model's parameters: abrupt faults, and gradual faults, where parameters change gradually over time. The faults causing significant increase in consumption are correctly identified by the proposed method. Moreover, an assessment of the impact of the faults is reported, showing up to 8% increase in the ventilation unit energy consumption. Finally, detection of gradual faults has promising applications in faults prediction.

Fault Detection of Areo-engine Actuator Based on Adaptive Radial Basic Function Observer

Aiming at the fault detection problem of aero-engine actuators, a fault diagnosis method for aero-engine actuators based on adaptive RBF network observer is proposed. Firstly, the system model is established for the faulty actuator, and then the observer-based actuator fault diagnosis principle is established. Secondly, for the problem that the traditional observer is not sensitive to the gradual fault, the nonlinear approximation characteristics of the RBF network are used. The adaptive fault observer design method based on RBF network is applied. At the same time, the sufficient conditions for the convergence of the observer error system are given by using the Lyapunov stability analysis method. Finally, the simulation experiments of different types of actuators for a turbofan engine are carried out. The results show that the adaptive RBF observer designed in this paper is more effective in detecting faults and has certain fault estimation ability.

A Novel Approach for Fault Detection in Integrated Navigation Systems

Detecting subsystem faults quickly is critical to the accuracy and reliability of integrated navigation systems. This paper, therefore, proposes an effective approach based on the novel test statistic to detect faults. Machine learning is introduced to estimate the innovation and its variance of local filter. The estimates combined with the actual ones are used to construct the test statistic, which is then proved to obey chi-square distribution. Thus fault detection can be realized by chi-square test. However, the special structure of the test statistic makes it sensitive to faults, even to the gradual faults. The experimental results demonstrate that the approach can detect faults quickly. Especially for gradual fault detection, the proposed test statistic has a marked superiority compared with the traditional test statistic of residual chi-square test.