Prognostics of Damage Accrual in SSL Luminaires and Drivers Subjected to HTSL Accelerated Aging

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Peter Sakalaukus ◽  
Lynn Davis
Keyword(s):  
High Temperatures ◽  
Health Management ◽  
Light Output ◽  
Accelerated Aging ◽  
Luminous Flux ◽  
Remaining Useful Life ◽  
Department Of Energy ◽  
Equivalent Series Resistance ◽  
Damage Accrual ◽  
Useful Life

This paper will show an investigation of off-the-shelf luminaires with the focus on the LED electronic drivers, specifically the aluminum electrolytic capacitors (AECs), that have been aged using high temperature shelf life (HTSL) testing of 135°C in order to prognosticate the remaining useful life of the luminaires. Luminaires have the potential of seeing excessive temperatures when being transported across the country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects being stored at high temperatures for a prolonged period of time will have on the usability and survivability of these devices. The U.S. Department of Energy has made a long term commitment to advance the efficiency, understanding and development of solid-state lighting (SSL) and is making a strong push for the acceptance and use of SSL products. In this work, the four AECs of three different types inside each LED electronic driver were studied. The change in capacitance and the change in equivalent series resistance (ESR) of the AECs were measured and considered to be a leading indication of failure of the LED system. These indicators were used to make remaining useful life predictions to develop an algorithm to predict the end of life of the AECs. The luminous flux of a pristine downlight module was also monitored using each LED electronic driver that was subjected to HTSL through the progression of the testing to determine a correlation between the light output of the lamp and the failing components of the LED electronic driver. Prognostic and Health Management (PHM) is a useful tool for assessment of the remaining life of electrical components and is demonstrated for AECs in this work.

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.

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.

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.

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.

scholarly journals Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Kun Wang ◽  
Yi Cheng ◽  
Ke Song
Keyword(s):  
Deep Learning ◽  
Working Conditions ◽  
Health Management ◽  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Key Technology ◽  
Life Estimation ◽  
Useful Life ◽  
New Generation ◽  
Deep Learning Model

The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction from the perspective of deep learning. Therefore, we propose a novel joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features. Both parts are jointly trained within a regression module, which implies that the proposed approach differs from traditional ensemble learning models. It is applied on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from the Prognostics Center of Excellence at NASA Ames, and satisfactory results are obtained, especially under complex working conditions.

Reliability Measurement

2016 ◽  
pp. 192-229
Keyword(s):  
Accelerated Aging ◽  
Theoretical Concept ◽  
Remaining Useful Life ◽  
Time To Failure ◽  
Operating Process ◽  
Accelerated Aging Test ◽  
Aging Test ◽  
Useful Life ◽  
Reliability Measurement ◽  
Standard Tests

After calculation of reliability, the system is constructed. It is important to measure the calculated useful life of the system. Reliability measurement tests can also be used for the converter operating in service to estimate the remaining useful life of the converter. In this chapter, various methods of tests for this goal are presented. The main approach is accelerated aging test that reduce the time needed for failure in a system. In this method, the device is tested under conditions beyond its defined nominal specifications. Limits for this harsh condition is determined based on the calculations which are presented in chapters 3 and 4. If a problem occurs in implementing and operating process of the converter, accelerated aging tests decrease the time to failure. Theoretical concept of accelerated aging tests is described. Standard tests of electric power converters are presented. Equipment and test chambers for standard tests are explained. These tests contain all four various failure factors which are presented in chapter 2. Sample industrial examples are presented to demonstrate the procedure of the tests. Some accelerated aging tests may lead to destruction of the converter. Difference between destructive and nondestructive tests is presented. Sample devices after accelerated aging tests are shown. Measuring devices for system parameter identification are introduced. Various types of tests are expressed in details for some of the most important tests like electric withstand tests.

scholarly journals Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guisheng Hou ◽  
Shuo Xu ◽  
Nan Zhou ◽  
Lei Yang ◽  
Quanhao Fu
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Health Management ◽  
Remaining Useful Life ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Useful Life ◽  
Prediction Approach

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.

scholarly journals In-situ remaining useful life prediction of aircraft auxiliary power unit based on quantitative analysis of on-wing sensing data

2020 ◽  
Vol 12 (3) ◽  
pp. 168781402091147
Author(s):  
Liansheng Liu ◽  
Qing Guo ◽  
Lulu Wang ◽  
Datong Liu
Keyword(s):  
Power Unit ◽  
Life Prediction ◽  
Health Management ◽  
Remaining Useful Life ◽  
Maintenance And Repair ◽  
Sensing Data ◽  
Auxiliary Power Unit ◽  
Auxiliary Power ◽  
Useful Life

The in-situ prognostics and health management of aircraft auxiliary power unit faces difficulty using the sparse on-wing sensing data. As the key technology of prognostics and health management, remaining useful life prediction of in-situ aircraft auxiliary power unit is hard to achieve accurate results. To solve this problem, we propose one kind of quantitative analysis of its on-wing sensing data to implement remaining useful life prediction of auxiliary power unit. Except the most important performance parameter exhaust gas temperature, the other potential parameters are utilized based on mutual information, which can be used as the quantitative metric. In this way, the quantitative threshold of mutual information for enhancing remaining useful life prediction result can be determined. The implemented cross-validation experiments verify the effectiveness of the proposed method. The real on-wing sensing data of auxiliary power unit for experiment are from China Southern Airlines Company Limited Shenyang Maintenance Base, which spends over $6.5 million on auxiliary power unit maintenance and repair each year for the fleet of over 500 aircrafts. Although the relative improvement is not too large, it is helpful to reduce the maintenance and repair cost.

scholarly journals A New Data-Driven Approach for Power IGBT Remaining Useful Life Estimation Based On Feature Reduction Technique and Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1571
Author(s):  
Adla Ismail ◽  
Lotfi Saidi ◽  
Mounir Sayadi ◽  
Mohamed Benbouzid
Keyword(s):  
Neural Network ◽  
Accelerated Aging ◽  
Principal Component ◽  
Life Time ◽  
Time Domain Analysis ◽  
Energy System ◽  
Remaining Useful Life ◽  
Feature Reduction ◽  
Operating Conditions ◽  
Useful Life

The insulated gate bipolar transistor (IGBT) is a crucial component of power converters (PCVs) and is commonly used in several PCVs topologies. On the other hand, the investigation and the study of the IGBT component show several changes within its behavior and lifetime, while this component is highly influenced by the operating conditions. Indeed, the monitoring of this component is necessary to minimize unexpected downtime of the wind energy system (WES). However, an accurate prediction of IGBTs remaining useful life (RUL) is the key enabler for life-time-optimized operation. Consequently, this work proposes a new prognostic approach for online IGBTs monitoring that adopts the time-domain analysis to extract useful information that is used as an input in the generation of the health indicator. Moreover, this approach is based on combining both of principal component analysis (PCA) technique and the feedforward neural network (FFNN) technique. PCA is used to reduce features extracted from IGBTs and the FFNN is implemented to achieve online regression of the trend parameter obtained from the PCA technique. To investigate and evaluate the performance of our idea we used the NASA Ames Laboratory Prognostics Center of Excellence IGBTs accelerated aging database. Finally, the achieved results clearly show the strength of the new trend parameter for IGBTs RUL prediction. The most notable strong correlation within the proposed approach is in relation to accuracy value, with an acceptable average accuracy rate of 60.4%.

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