Application Driven Reliability Research of Next Generation for Automotive Electronics: Challenges and Approaches

2017 ◽  
Author(s):  
Sven Rzepka ◽  
Alexander Otto ◽  
Dietmar Vogel ◽  
Rainer Dudek
Keyword(s):  
Autonomous Vehicles ◽  
Failure Modes ◽  
Health Management ◽  
Remaining Useful Life ◽  
Automotive Electronics ◽  
Stress Tests ◽  
Sensors And Actuators ◽  
Constant Attention ◽  
Smart Systems ◽  
Lighting Energy

The revolutionary changes in automotive industry towards fully connected automated electrical vehicles necessitates developments in automotive electronics at unprecedented speed. Signal, control, and power electronics will heterogeneously be integrated at minimum space with sensors and actuators to form highly compact and ultra-smart systems for functions like traction, lighting, energy management, computation, and communication. Most of these systems will be highly safety relevant with the requirements in system availability exceeding today’s already high automotive standards. Other than the human drivers of today, passengers in the automated car do not pay constant attention to the driving actions of the vehicle. Hence, reliability research is massively challenged by the new automotive applications. Guaranteeing the specified lifetime at statistical average is no longer sufficient. Assuring that no failure of an individual safety relevant part occurs unexpectedly, becomes most important. The paper surveys the priority actions underway to cope with the tremendous challenges. It highlights practical examples in all three directions of reliability research. i) Experimental reliability tests and physical analyses: New and highly efficient accelerated stress tests are able to cover the complex and multi-fold loading situation in the field. New analytics techniques can identify the typical failure modes and their physical root causes. ii) Virtual techniques: Schemes of validated simulations allow capturing the physics of failure proactively in the design for reliability process. iii) Prognostics health management (PHM): A new concept is introduced for adding a minimum of PHM features at the various levels of automotive electronics to provide functional safety as required for autonomous vehicles. This way, the new generation of reliability methods will continuously provide estimates of the remaining useful life (RUL) for each relevant part under the actual use conditions to allow triggering maintenance in time.

Application-Driven Reliability Research of Next Generation for Automotive Electronics: Challenges and Approaches

2018 ◽  
Vol 140 (1) ◽  
Author(s):  
Sven Rzepka ◽  
Alexander Otto ◽  
Dietmar Vogel ◽  
Rainer Dudek
Keyword(s):  
Autonomous Vehicles ◽  
Failure Modes ◽  
Health Management ◽  
Remaining Useful Life ◽  
Automotive Electronics ◽  
Stress Tests ◽  
Sensors And Actuators ◽  
Constant Attention ◽  
Smart Systems ◽  
Lighting Energy

The revolutionary changes in automotive industry toward fully connected automated electrical vehicles necessitate developments in automotive electronics at unprecedented speed. Signal, control, and power electronics will heterogeneously be integrated at minimum space with sensors and actuators to form highly compact and ultra-smart systems for functions like traction, lighting, energy management, computation, and communication. Most of these systems will be highly safety relevant with the requirements in system availability exceeding today's already high automotive standards. Unlike the human drivers of today, passengers in the automated car do not pay constant attention to the driving actions of the vehicle. Hence, reliability research is massively challenged by the new automotive applications. Guaranteeing the specified lifetime at statistical average is no longer sufficient. Assuring that no failure of an individual safety relevant part occurs unexpectedly becomes most important. The paper surveys the priority actions underway to cope with the tremendous challenges. It highlights practical examples in all three directions of reliability research: (i) Experimental reliability tests and physical analyses: New and highly efficient accelerated stress tests are able to cover the complex and multifold loading situation in the field. New analytics techniques can identify the typical failure modes and their physical root causes; (ii) Virtual techniques: Schemes of validated simulations allow capturing the physics of failure (PoF) proactively in the design for reliability (DfR) process; and (iii) Prognostics health management (PHM). A new concept is introduced for adding a minimum of PHM features at various levels of automotive electronics to provide functional safety as required for autonomous vehicles. This way, the new generation of reliability methods will continuously provide estimates of the remaining useful life (RUL) for each relevant part under the actual use conditions to allow triggering maintenance in time

Knowledge Based Qualification Process to Evaluate Vibration Induced Failures in Electronic Components

2017 ◽  
Author(s):  
Karumbu Meyyappan ◽  
Milena Vujosevic ◽  
Qifeng Wu ◽  
Pramod Malatkar ◽  
Charles Hill ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Failure Modes ◽  
Damage Model ◽  
Automotive Electronics ◽  
Electronic Components ◽  
Standard Requirement ◽  
Reliability Models ◽  
Knowledge Based ◽  
Industry Standards ◽  
Solder Joint Fatigue

Electronic products used in autonomous vehicles can be subjected to harsh road conditions. Transportation induced vibration is one such reliability risk to be addressed as part of qualification. Vibration use data and reliability models are very extensively studied for fully packaged systems exposed to vibration risks during shipping. MIL-STD-810G and ISTA4AB are some of the industry standards that address these risks. On the other hand, USCAR-2 and GMW-3172 are couple of standards that may be more relevant for electronics used in automotive applications, where electronic components are exposed to vibration risks during their entire lifetime. Even though the usage model and duration for fully packaged systems in shipping and automotive electronics are different, the source of energy (road conditions), driving the risks are similar. The industry standards based damage model appear to be generic, covering a wide variety of products. In this paper, a knowledge based qualification (KBQ) framework, is used to map use conditions to accelerated test requirements for two failure modes: solder joint fatigue and socket contact fretting. The mechanisms chosen are distinct with different damage metric and drivers. The KBQ obtained qualification requirements were discussed relative to standard requirement with the objective to verify how well industry standard models reflect field reliability risks. For the chosen failure mechanisms and use condition data, it was observed that the industry standards lead to erroneous conclusions about vibration risk in the field.

scholarly journals Prognostics and Health Management of an Automated Machining Process

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Cheng He ◽  
Jiaming Li ◽  
George Vachtsevanos
Keyword(s):  
Particle Filtering ◽  
Failure Modes ◽  
Health Management ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Prediction Algorithm ◽  
Prognostics And Health Management ◽  
Modeling Framework ◽  
Detection Scheme ◽  
Machine Failure

Machine failure modes are presenting a major burden to the operator, the plant, and the enterprise causing significant downtime, labor cost, and reduced revenue. New technologies are emerging over the past years to monitor the machine’s performance, detect and isolate incipient failures or faults, and take appropriate actions to mitigate such detrimental events. This paper addresses the development and application of novel Prognostics and Health Management (PHM) technologies to a prototype machining process (a screw-tightening machine). The enabling technologies are built upon a series of tasks starting with failure analysis, testing, and data processing aimed to extract useful features or condition indicators from raw data, a symbolic regression modeling framework, and a Bayesian estimation method called particle filtering to predict the feature state estimate accurately. The detection scheme declares the fault of a machine critical component with user specified accuracy or confidence and given false alarm rate while the prediction algorithm estimates accurately the remaining useful life of the failing component. Simulation results support the efficacy of the approach and match well the experimental data.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

scholarly journals Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors

2021 ◽  
Vol 9 (1) ◽  
pp. 47
Author(s):  
Magnus Gribbestad ◽  
Muhammad Umair Hassan ◽  
Ibrahim A. Hameed
Keyword(s):  
Transfer Learning ◽  
Data Transfer ◽  
Health Management ◽  
Correct Diagnosis ◽  
Vital Role ◽  
Research Field ◽  
Remaining Useful Life ◽  
Prognostic Models ◽  
Classification Problems ◽  
Air Compressors

Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. Due to the requirements of system safety and reliability, the correct diagnosis or prognosis of abnormal condition plays a vital role in the maintenance of industrial systems. It is expected that new requirements in regard to autonomous ships will push suppliers of maritime equipment to provide more insight into the conditions of their systems. One of the stated challenges with these systems is having enough run-to-failure examples to build accurate-enough prognostic models. Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. In this paper, various deep learning models are used to predict the remaining useful life (RUL) of air compressors. Here, transfer learning is applied by building a separate prognostics model trained on turbofan engines. It has been found that several of the explored transfer learning architectures were able to improve the predictions on air compressors. The research results suggest transfer learning as a promising research field towards more accurate and reliable prognostics.

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

2013 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Pingfeng Wang ◽  
Chao Hu
Keyword(s):  
Sampling Method ◽  
Failure Time ◽  
Health Management ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Statistical Relationship ◽  
Cooling Fans ◽  
On Line

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction

2021 ◽  
Author(s):  
Mohammad Rubyet Islam ◽  
Peter Sandborn
Keyword(s):  
Health Management ◽  
Standard Test ◽  
Remaining Useful Life ◽  
Software Systems ◽  
Prognostics And Health Management ◽  
Performance Parameters ◽  
Statistical Validation ◽  
Useful Life ◽  
Test Beds

Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software. Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.

Prognostics Health Management Model for LED Package Failure Under Contaminated Environment

2015 ◽  
Author(s):  
Pradeep Lall ◽  
Hao Zhang ◽  
Lynn Davis
Keyword(s):  
Health Management ◽  
Luminous Flux ◽  
The State ◽  
Remaining Useful Life ◽  
Integrating Sphere ◽  
State Estimator ◽  
Acceptable Error ◽  
Color Shift ◽  
Useful Life ◽  
The Impact

The reliability consideration of LED products includes both luminous flux drop and color shift. Previous research either talks about luminous maintenance or color shift, because luminous flux degradation usually takes very long time to observe. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphor-converted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable error-bounds (20%) after a short training of the estimator.

scholarly journals Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Aisong Qin ◽  
Qinghua Zhang ◽  
Qin Hu ◽  
Guoxi Sun ◽  
Jun He ◽  
...  
Keyword(s):  
Health Management ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Programming Algorithm ◽  
Wiener Model ◽  
New Approach ◽  
Degradation Modeling ◽  
Proposed Model ◽  
Useful Life ◽  
Degradation Indicator

Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.

Aircraft Fleet Maintenance Planning Using Combined Cost Benefit Model and Branch and Bound

2013 ◽  
Author(s):  
Behnam Razavi ◽  
Farrokh Sassani
Keyword(s):  
Branch And Bound ◽  
Health Management ◽  
Cost Benefit ◽  
Remaining Useful Life ◽  
Maintenance Planning ◽  
Flight Delays ◽  
Engine Operation ◽  
Maintenance And Repair ◽  
Flight Schedules ◽  
Useful Life

The tasks of maintenance and repair without optimal planning can be costly and result in prolonged maintenance times, reduced availability and possible flight delays. Aircraft manufacturers and maintainers see significant benefits in constantly improving Health Management and Maintenance (HMM) practices by deploying the most effective maintenance planning strategies. The planning of the maintenance and repair is a complex task due to chain dependency of engines to aircraft, and aircraft to the flight schedules. This paper presents a scheduling method for determining the time of maintenance based on the historical engine operation data in order to maximize the use of estimated remaining useful life of the engines as well as lowering the cost and duration of the downtime. The Time-on-Wing (TOW) data is used in conjunction with probability density functions to determine the shape of the respective distribution of the time of maintenance to minimize the loss of expected remaining useful life. Data from each engine with most chance of failure is then selected and fed into an extended Branch and Bound (B&B) routine to determine the best optimum sequence for entering the facility in order to minimize the waiting time.

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