Lifetime estimation of tantalum capacitor for mobile applications using empirical and experimental techniques: a DOE approach

PurposeCapacitors are one of the most common passive components on a circuit board. From a tiny toy to substantial satellite, a capacitor plays an important role. Untimely failure of a capacitor can destruct the entire system. This research paper targets the reliability assessment of tantalum capacitor, to reduce e-waste and enhance its reusable capability.Design/methodology/approachThe residual lifetime of a tantalum capacitor is estimated using the empirical method, i.e. military handbook MILHDBK2017F, and validated using an experimental approach, i.e. accelerated life testing (ALT). The various influencing acceleration factors are explored, and experiments are designed using Taguchi's approach. Empirical methods such as the military handbook is used for assessing the reliability of a tantalum capacitor, for ground and mobile applications.FindingsAfter exploring the lifetime of a tantalum capacitor using empirical and experimental techniques, an error analysis is conducted, which shows the validity of empirical technique, with an accuracy of 95.21%.Originality/valueThe condition monitoring and health prognostics of tantalum capacitors, for ground and mobile applications, are explored using empirical and experimental techniques, which warns the user about its residual lifetime so that the faulty component can be replaced in time.

Multilayer Architecture for Fault Diagnosis of Embedded Systems

This work presents a multilayer architecture for fault diagnosis in embedded systems based on formal modeling of Discrete Event Systems (DES). Most works on diagnosis of DES focus in faults of actuators, which are the devices subject to intensive wear in industry. However, embedded systems are commonly subject to cost reduction, which may increase the probability of faults in the electronic hardware. Further, software faults are hard to track and fix, and the common solution is to replace the whole electronic board. We propose a modeling approach which includes the isolation of the source of the fault in the model, regarding three layers of embedded systems: software, hardware, and sensors & actuators. The proposed method is applied to a home appliance refrigerator and after exhaustive practical tests with forced fault occurrences, all faults were diagnosed, precisely identifying the layer and the faulty component. The solution was then incorporated into the product manufactured in industrial scale.

Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.

Statistical and intelligent reliability analysis of multi-layer ceramic capacitor for ground mobile applications using Taguchi’s approach

PurposeAlthough Multi-Layer Ceramic Capacitors (MLCC) are known for its better frequency performance and voltage handling capacity, but under various environmental conditions, its reliability becomes a challenging issue. In modern era of integration, the failure of one component can degrade or shutdown the whole electronic device. The lifetime estimation of MLCC can enhance the reuse capability and furthermore, reduces the e-waste, which is a global issue.Design/methodology/approachThe residual lifetime of MLCC is estimated using empirical method i.e. Military handbook MILHDBK2017F, statistical method i.e. regression analysis using Minitab18.1 as well as intelligent technique i.e. artificial neural networks (ANN) using MATLAB2017b. ANN Feed-Forward Back-Propagation learning with sigmoid transfer function [3–10–1–1] is considered using 73% of available data for training and 27% for testing and validation. The design of experiments is framed using Taguchi’s approach L16 orthogonal array.FindingsAfter exploring the lifetime of MLCC, using empirical, statistical and intelligent techniques, an error analysis is conducted, which shows that regression analysis has 97.05% accuracy and ANN has 94.07% accuracy.Originality/valueAn intelligent method is presented for condition monitoring and health prognostics of MLCC, which warns the user about its residual lifetime so that faulty component can be replaced in time.

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

Anticipatory Troubleshooting

Troubleshooting is the process of diagnosing and repairing a system that is behaving abnormally. It involves performing various diagnostic and repair actions. Performing these actions may incur costs, and traditional troubleshooting algorithms aim to minimize the costs incurred until the system is fixed. Prognosis deals with predicting future failures. We propose to incorporate prognosis and diagnosis techniques to solve troubleshooting problems. This integration enables (1) better fault isolation and (2) more intelligent decision making with respect to the repair actions to employ to minimize troubleshooting costs over time. In particular, we consider an anticipatory troubleshooting challenge in which we aim to minimize the costs incurred to fix the system over time, while reasoning about both current and future failures. Anticipatory troubleshooting raises two main dilemmas: the fix–replace dilemma and the replace-healthy dilemma. The fix–replace dilemma is the question of how to repair a faulty component: fixing it or replacing it with a new one. The replace-healthy dilemma is the question of whether a healthy component should be replaced with a new one in order to prevent it from failing in the future. We propose to solve these dilemmas by modeling them as a Markov decision problem and reasoning about future failures using techniques from the survival analysis literature. The resulting algorithm was evaluated experimentally, showing that the proposed anticipatory troubleshooting algorithms yield lower overall costs compared to troubleshooting algorithms that do not reason about future faults.

Does One Bad Machine Spoil the Bunch?: A Review of Trust in Multiple-Component Systems

Human-automation interactions are rapidly transitioning from single-component automated systems to multiple-component systems. The human-automation literature has yet to adequately explore trust within multiple-component systems. A currently unanswered question is whether one faulty component causes an operator to lose trust in that one component (Component-Specific Trust; CST) or in every component in the system (System-Wide Trust; SWT). The goals of this paper were to 1) summarize the current work on trust in multiple-component systems, and 2) identify any trends that emerge during the literature review. We reviewed 17 experimental studies that tested whether operators tend to adopt CST or SWT under different conditions. Overall, most studies suggest that operators adopt SWT. However, studies that provided the operator with high decisional freedom and more time with the automated systems suggest that CST is the dominant strategy. Future work should explicitly test these and other variables that may promote users to adopt CST.

Cascade stress-strength system Reliability Estimation of Inverse Rayleigh Distribution

The electronic devices, equipment and complex machines used in many fields such as telecommunications, medicine, astronautics and others are all subject to malfunctions, which cause material and moral losses, waste of time and other damages. Hence the importance of reliability issue in our working life by evaluating the performance and efficiency of these systems and Measuring the reliability of any device will be the basis for the development of most of these devices . Then In this paper will discussed the Estimation of Reliability Rn for cascade system when the stress and strength are Inverse Rayleigh distributed random variables. under-voltage rating. The cascade system is a redundant component system, which is a redundant component with under-voltage rating and independently distributed power, in which the redundant component replaces the faulty component.. Cascade system is a special case of Stress- Strength models system. Also we discussed the Estimation of Marginal Reliabilities R1, R2 and R3 for Cascade system by three estimation methods (Max. likelihood, Weighted Least Square, Least Square) and Compare between the estimators of R4. © 2018 JASET, International Scholars and Researchers Association

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

Vibration-based Condition Monitoring of Industrial Drivetrains Operating under Non-stationary Conditions

This research is focused on advancing the state-of-the art in fault diagnostics of industrial drivetrains. It is proposed to develop a fault detection algorithm and fault severity prediction model for critical drivetrain components using measured vibration signals. The fault detection algorithm will be developed to overcome the challenge of extracting reliable fault information for drivetrains operating under speed and load fluctuations, whereas the severity prediction model will aid in predicting the degradation level of a faulty component. The results of this research can, therefore, help in improving the availability of industrial drivetrains, by providing a dependable platform to the maintenance personnel for proper decision-making.