Yiqian Cui

Analog circuits fault diagnosis using multi-valued Fisher's fuzzy decision tree (MFFDT)

Summary Fault diagnosis of analog circuits is more challenging compared with digital circuits as a result of the parametric deviation and the difficulty in signal discretization. There still lacks effective approaches to realize reliable fault detection and isolation for a comprehensive diagnosis. A new fault diagnosis technique called multi-valued Fishers fuzzy decision tree (MFFDT) is proposed in this paper to solve the problem. This technique uses the decision tree as the diagnosis model and incorporates the Fishers linear discriminant principles. The fuzzification mechanism is devised to discretize the input monitoring data. The proposed MFFDT method is composed of two aspects: decision tree training and real fault diagnosis processes. The former uses the benchmark data to train a decision tree, while the latter sends the monitoring data into the decision tree to generate diagnosis results. The proposed method is validated using simulated data and the real-time data for an active filter circuit and an audio amplifying circuit. The comparative analysis is also presented to evaluate diagnosis performances. Copyright

An analytical model of electronic fault diagnosis on extension of the dependency theory

Based on the D-matrix model, the dependency theory is widely used in the field of fault diagnosis to model the fault flows in complex electronic systems. However, the traditional dependency model can only handle a single fault; it fails to recognize and diagnose multiple faults. In addition, it is not tolerant with system structural or functional changes. These inherent weaknesses of the traditional dependency theory may lead to unsatisfactory acquisition of the diagnosis results. To solve the problem, an improved dependency model is invented as novel analytic diagnosis model to better describe the relationships between faults and tests. The system fault diagnosis based on the improved dependency model is formulated as an optimization problem with binary logic operations where all the fault hypotheses are tested. The calculation process consists of three steps: establishment of the objective function, determination of the nominal states, and determination of the expected states. Finally, the proposed method is demonstrated via an avionic processor case using the improved dependency model. The optimization-based fault diagnosis problem is formulated and the optimal solution is obtained. The diagnosis result demonstrates that the proposed method is successful on performance assessment and fault diagnosis.

Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN)

Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction.

Multi-State Adaptive BIT False Alarm Reduction Under Degradation Process

Built-in tests (BITs) are widely used in mechanical systems to detect and diagnose a fault, whereas the BIT false alarms bring much trouble for precise fault diagnosis and logistics/maintenance arrangement. The false alarm phenomenon is related to the degradation over time, and the false alarm evolution process can be typically divided into three stages. This paper proposes a condition-based multistage false alarm detection and reduction method for mechanical systems. The stages are clarified according to the degradation level and the false alarm severity. The dividing boundaries of the stages are optimized using soft margin one-versus-rest support vector machine (SVM) classifiers. The associated intermediate stage is the intense period of false alarms, and the dynamic Bayesian network inference model is developed to satisfy the requirements of accurate false alarm diagnosis. To achieve the goal of false alarm suppression, the top-level BIT outputs are updated with the original BIT alarms and the identified probable states. Finally, the proposed approach is demonstrated in the application study of a milling machine and the well-round experimental results are analyzed.

Analog Circuit Test Point Selection Incorporating Discretization-Based Fuzzification and Extended Fault Dictionary to Handle Component Tolerances

Analog circuit test point selection aims to find the least number of test points that can isolate all the fault modes (including the fault-free case). The fault dictionary, which uses the integer-valued codes to represent the diagnosability of a specific test point, is very popular and saves computation efforts. However, the classical fault dictionary has a limited ability to handle the component tolerances and continuous-valued monitoring variables. To solve the problem, the approach of clustering-based discretization (CBD) is used to abstract the information of data samples distribution. We also develop a new fault dictionary construction technique called extended fault dictionary (EFD). An element of EFD is a set containing either a single integer code or multiple integer codes. The fault isolation rules are redefined, and a novel entropy measure is created in line with CBD of the continuous values. The practical test point selection procedures are presented, which avoids the likelihood to include a redundant test point. Finally, two application studies of circuit test point election are presented, showing that the proposed method provides an effective implementation option for the engineering practice of circuit diagnosis.

Intermittent failure process and false alarm interaction modelling of threshold-based monitoring built-in tests (BITs)

Built-in tests (BITs) are widely used in manufacturing and production systems to find whether system failures occur, whereas the problem of BIT false alarms caused by intermittent failures adds to much trouble for the precise failure detection and diagnosis. Fighting with false alarms caused by intermittent failures is an urgent issue. However, the nature and temporal regularity of intermittent failures are not fully exploited, as well as the relationship between intermittent failure and BIT false alarms. The present paper introduces the method of constructing failure test profile for false alarm assessments. Probabilistic models are proposed of the failure evolution process, as well as the interactions between intermittent failures and false alarms. The false alarm time expectation is derived with the given model, serving as the foundation for the optimisation problem to find the best test threshold to enable the highest BIT capability. A numerical analysis is made to illustrate the proposed model and examine the threshold determination method. An application study is also carried out to show how the model can be applicable in real engineering practices.

Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

Temporal information is fundamental in model-based fault diagnosis, and the alarm-processing problem is to interpret the alarm sequences to infer the type and time of fault event occurrences. There can be cycles or feedback loops in real power systems, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the improved temporal constraint network. The reasoning method is dependent on the time point and time distance information, with which the fault motivators (or actuators) and fault responders (or victims) can be identified. The system fault event reasoning and diagnosis are formulated as an optimization problem with the fault hypotheses being tested. The calculation process consists of three steps: 1) establishment of the objective function, 2) determination of the fault propagation paths, and 3) determination of the expected states under a given hypothesis. Finally, the proposed method is demonstrated via a power system example, which is verified to be successful in performance assessment and fault diagnosis.

Quantum Assimilation-Based State-of-Health Assessment and Remaining Useful Life Estimation for Electronic Systems

State-of-health (SOH) assessment and remaining useful life (RUL) estimation are among the key issues in prognostics and health management (PHM) for electronic systems. Unlike mechanical systems, the homogeneity of the fault modes is quite low for electronic systems. Seen at the system level, there are also multiple and uncertain fault modes for electronic products. In this paper, we propose a novel methodology for system-level SOH assessment and RUL estimation inspired by the quantum mechanics disciplines, where there is no requirement to distinguish the fault modes from the fault development patterns. It is developed on the analogy that the healthy data points tend to move to the lower potential energy positions, and the subhealthy or unhealthy ones tend to be repelled from such positions. The fault development paths are located on the potential surface. The Fermi-Dirac health descriptor (FDHD) is defined based on the pseudowave function and potential function. Based on FDHD, the SOH assessment and RUL estimation algorithm are developed to track the existing fault development paths. Finally, the proposed method is verified in an experiment using power conversion board with a detailed analysis of the SOH assessment results and RUL estimation performances.

Discrete Event Logistics Systems (DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation

The technique of two-step RUL estimation in DELS simulation modeling is exploited.The analytical deterioration model is developed as the foundation of two-step RUL estimation.The simulation sequential logics of the DELS model using two-step estimation are discussed.The effectiveness of the method is verified with a case analysis in the implementation study. Prognostics and Health Management (PHM) exerts an essential influence on the spare supply process and the maintenance activities. Discrete Event Logistics Systems (DELS) simulation model facilitates a better understanding of the maintenance and logistics/support systems. Previous DELS models treat the RUL estimation as a one shot event. However, the treatment would be rough to coordinate the logistics and maintenance activities, and the estimated RUL result would not be sufficiently reliable. In this paper, we propose the principle and operational technique of two-step RUL estimation for the DELS simulation model. Two-step RUL estimation starts with the component RUL modeling subject to a continuous accumulation of degradation. The component deterioration is modeled using a time-dependent stochastic process, which combines the linear degradation path with a random effect. Besides, the sequential logics of the DELS simulation model incorporating two-step RUL estimation is exploited in the local behavior study. Finally, the proposed technique is testified with a case study via the DELS simulation implementation, showing that the performance using two-step RUL estimation outperforms traditional one-step RUL estimation.

System maintenance drive simulation method in intermittent operations under PHM supervision

Prognosis and Health Management (PHM) has exerted a profound impact on the integrated maintenance and support system. By monitoring the fault evolution of the parts or components, the potential fault could be predicted, and the corresponding maintenance and support decisions could be deducted. PHM has transferred the traditional maintenance mode to the Condition-based Maintenance (CBM). In order to validate the availability and the effectiveness of the integrated maintenance and support system, the computer simulation method is widely used. The maintenance drive serves as the starting point of a new cycle of maintenance and support activity in the object-oriented simulation model. Traditional simulations based on the system failure rate or mean time between failures (MTBF) would not ensure an accurate result when PHM functions. This paper makes a discussion on that problem. The concept of fault evolution index (FEI) is put forth in the paper, and several intermittent FEI evolution models are given for simulation reference. In addition, the paper gives a detailed process of modeling and simulating the maintenance drive.