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.

LED Lumen Degradation and Remaining Life Under Exposure to Temperature and Humidity

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Hao Zhang
Keyword(s):  
Solid State ◽  
Particle Filter ◽  
Luminous Flux ◽  
Remaining Useful Life ◽  
Integrating Sphere ◽  
Harsh Environment ◽  
Forward Voltage ◽  
Acceptable Error ◽  
Forward Current ◽  
Useful Life

The development of light-emitting diode (LED) technology has resulted in widespread solid state lighting use in consumer and industrial applications. Previous researchers have shown that LEDs from the same manufacturer and operating under same use-condition may have significantly different degradation behavior. Applications of LEDs to safety critical and harsh environment applications necessitate the characterization of failure mechanisms and modes. This paper focuses on a prognostic health management (PHM) method based on the measurement of forward voltage and forward current of bare LED under harsh environment. In this paper experiments have been done on single LEDs subjected to combined temperature-humidity environment of 85°C, 85% relative humidity. Pulse width modulation (PWM) control method has been employed to drive the bare LED in order to reduce the heat effect caused by forward current and high frequency (300Hz). A data acquisition system has been used to measure the peak forward voltage and forward current. Test to failure (luminous flux drops to 70 percent) data has been measured to study the effects of high temperature and humid environment loadings on the bare LEDs. A solid state cooling method with a peltier cooler has been used to control the temperature of the LED in the integrating sphere when taking the measurement of luminous flux. The shift of forward voltage forward current curve and lumen degradation has been recorded to help build the failure model and predict the remaining useful life. Particle filter has been employed to assess the remaining useful life (RUL) of the bare LED. Model predictions of RUL have been correlated with experimental data. Results show that prediction of remaining useful life of LEDs, made by the particle filter model works with acceptable error-bounds. The presented method can be employed to predict the failure of LED caused by thermal and humid stresses.

Assessment of Lumen Degradation and Remaining Life of LEDs Using Particle Filter

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Hao Zhang ◽  
Lynn Davis
Keyword(s):  
Solid State ◽  
Particle Filter ◽  
Luminous Flux ◽  
Remaining Useful Life ◽  
Integrating Sphere ◽  
Harsh Environment ◽  
Forward Voltage ◽  
Acceptable Error ◽  
Forward Current ◽  
Useful Life

The development of light-emitting diode (LED) technology has resulted in widespread solid state lighting use in consumer and industrial applications. Previous researchers have shown that LEDs from the same manufacturer and operating under same use-condition may have significantly different degradation behavior. Applications of LEDs to safety critical and harsh environment applications necessitate the characterization of failure mechanisms and modes. This paper focuses on a prognostic health management (PHM) method based on the measurement of forward voltage and forward current of bare LED under harsh environment. In this paper experiments have been done on single LEDs subjected to combined temperature-humidity environment of 85°C, 85% relative humidity. Pulse width modulation (PWM) control method has been employed to drive the bare LED in order to reduce the heat effect caused by forward current and high frequency (300Hz). A data acquisition system has been used to measure the peak forward voltage and forward current. Test to failure (luminous flux drops to 70 percent) data has been measured to study the effects of high temperature and humid environment loadings on the bare LEDs. A solid state cooling method with a peltier cooler has been used to control the temperature of the LED in the integrating sphere when taking the measurement of luminous flux. The shift of forward voltage forward current curve and lumen degradation has been recorded to help build the failure model and predict the remaining useful life. Particle filter has been employed to assess the remaining useful life (RUL) of the bare LED. Model predictions of RUL have been correlated with experimental data. Results show that prediction of remaining useful life of LEDs, made by the particle filter model works with acceptable error-bounds. The presented method can be employed to predict the failure of LED caused by thermal and humid stresses.

Implementation of a new inlet protection system into HEMS fleet

2020 ◽  
Vol 92 (1) ◽  
pp. 67-79
Author(s):  
Bartosz Stanisław Przybyła ◽  
Radoslaw Przysowa ◽  
Zbigniew Zapałowicz
Keyword(s):  
Health Management ◽  
Particle Separation ◽  
Lessons Learned ◽  
Remaining Useful Life ◽  
Operational Experience ◽  
Content Type ◽  
Useful Life ◽  
The Impact ◽  
Dust Ingestion ◽  
Selection Of

Purpose EC-135P2+ helicopters operated by Polish Medical Air Rescue are highly exposed to environmental particles entering engines when performing helicopter emergency medical services. This paper aims to assess the effectiveness of inlet barrier filters installed to protect the engines, including their impact on maintenance. Design/methodology/approach The organisation adopted a comprehensive set of measures to predict and limit the impact of dust ingestion including visual inspections, health management and engine trend monitoring based on ground power checks’ (GPC) results. Three alternative particle separation solutions were considered. Finally, helicopter inlets were modified to allow the selected filter system to be installed, which reduced the number of particles ingested by the engine and prevented from premature overhauls. Findings The analyses carried out enabled not only the selection of the optimal filtration solution and its seamless implementation into the fleet but also confirmed its efficiency. After installing the filters, engines’ lifetime is extended from 500 to 4,500 flight hours while operating costs and the number of maintenance tasks was reduced significantly. Originality/value Lessons learned from operational experience show that a well-matched particle separation system can mitigate accelerated engine deterioration even if the platform is continuously exposed to environmental particles. The remaining useful life of engines can be predicted using performance models and data from GPC.

Assessment of Lumen Degradation and Remaining Life of Light-Emitting Diodes Using Physics-Based Indicators and Particle Filter

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Pradeep Lall ◽  
Hao Zhang
Keyword(s):  
Solid State ◽  
Particle Filter ◽  
Luminous Flux ◽  
Remaining Useful Life ◽  
Integrating Sphere ◽  
Harsh Environment ◽  
Forward Voltage ◽  
Acceptable Error ◽  
Light Emitting ◽  
Forward Current

The development of light-emitting diode (LED) technology has resulted in widespread solid state lighting (SSL) use in consumer and industrial applications. Previous researchers have shown that LEDs from the same manufacturer and operating under same use-condition may have significantly different degradation behavior. Applications of LEDs to safety critical and harsh environment applications necessitate the characterization of failure mechanisms and modes. This paper focuses on a prognostic health management (PHM) method based on the measurement of forward voltage and forward current of bare LED under harsh environment. In this paper, experiments have been done on single LEDs subjected to combined temperature–humidity environment of 85 °C, 85% relative humidity. Pulse width modulation (PWM) control method has been employed to drive the bare LED in order to reduce the heat effect caused by forward current and high frequency (300 Hz). A data acquisition system has been used to measure the peak forward voltage and forward current. Test to failure (luminous flux drops to 70%) data has been measured to study the effects of high temperature and humid environment loadings on the bare LEDs. A solid state cooling method with a Peltier cooler has been used to control the temperature of the LED in the integrating sphere when taking the measurement of luminous flux. The shift of forward voltage forward current curve and lumen degradation has been recorded to help build the failure model and predict the remaining useful life (RUL). Particle filter has been employed to assess the RUL of the bare LED. Model predictions of RUL have been correlated with experimental data. Results show that prediction of RUL of LEDs, made by the particle filter model, works with acceptable error-bounds. The presented method can be employed to predict the failure of LED caused by thermal and humid stresses.

scholarly journals Impact of Prognostic Uncertainty in System Health Monitoring

2020 ◽  
Vol 6 (4) ◽  
Author(s):  
Robert M. Vandawaker ◽  
David R. Jacques ◽  
Jason K. Freels
Keyword(s):  
Health Monitoring ◽  
Health Management ◽  
Discrete Event ◽  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Sensor Data ◽  
False Alarms ◽  
Diagnostic Uncertainty ◽  
Useful Life ◽  
The Impact

Across many industries, systems are exceeding their intended design lives, whether they are ships, bridges or military aircraft. As a result failure rates can increase and unanticipated wear or failure conditions can arise. Health monitoring research and application has the potential to more safely lengthen the service life of a range of systems through utilization of sensor data and knowledge of failure mechanisms to predict component life remaining. A further benefit of health monitoring when combined across an entire platform is system health management. System health management is an enabler of condition based maintenance, which allows repair or replacement based on material condition, not a set time. Replacement of components based on condition can enable cost savings through fewer parts being used and the associated maintenance costs. The goal of this research is to show the management of system health can provide savings in maintenance and logistics cost while increasing vehicle availability through the approach of condition based maintenance.This work examines the impact of prediction accuracy uncertainty in remaining useful life prognostics for a squadron of 12 aircraft. The uncertainty in this research is introduced in the system through an uncertainty factor applied to the useful life prediction. An ARENA discrete event simulation is utilized to explore the effect of prediction error on availability, reliability, and maintenance and logistics processes. Aircraft are processed through preflight, flight, and post-flight operations, as well as maintenance and logistics activities. A baseline case with traditional time driven maintenance is performed for comparison to the condition based maintenance approach of this research.This research does not consider cost or decision making processes, instead focusing on utilization parameters of both aircraft and manpower. The occurrence and impact of false alarms on system performance is examined. The results show the potential availability, reliability, and maintenance benefits of a health monitoring system and explore the diagnostic uncertainty.

PHM of Leadfree Electronic Assemblies Using Resistance Spectroscopy With Sequential Importance Resampling

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe ◽  
Kai Goebel
Keyword(s):  
State Space ◽  
Feature Vector ◽  
Health Management ◽  
Residual Life ◽  
The State ◽  
Remaining Useful Life ◽  
Damage Initiation ◽  
State Variable ◽  
Useful Life ◽  
Electronic Assemblies

Electronic assemblies have been monitored using state-space vectors from resistance spectroscopy, phase-sensitive detection and particle filtering (PF) to quantify damage initiation, progression and remaining useful life of the electronic assembly. A prognostication health management (PHM) methodology has been presented for electronic components subjected to mechanical shock and vibration. The presented methodology is an advancement of the state-of-art, which presently focuses on reactive failure detection and provides limited or no insight into the system reliability and residual life. Previously damage initiation, damage progression, and residual life in the pre-failure space has been correlated with micro-structural damage based proxies, feature vectors based on time, spectral and joint time-frequency characteristics of electronics [Lall2004a–d, 2005a–b, 2006a–f, 2007a–e, 2008a–f]. Precise resistance measurements based on the resistance spectroscopy method have been used to monitor interconnects for damage and prognosticate failure [Lall 2009a,b, 2010a,b, Constable 1992, 2001]. In this paper, the effectiveness of the proposed particle filter and resistance spectroscopy based approach in a prognostic health management (PHM) framework has been demonstrated for electronics. The measured state variable has been related to the underlying damage state using non-linear finite element analysis. The particle filter has been used to estimate the state variable, rate of change of the state variable, acceleration of the state variable and construct a feature vector. The estimated state-space parameters have been used to extrapolate the feature vector into the future and predict the time-to-failure at which the feature vector will cross the failure threshold. Remaining useful life has been calculated based on the evolution of the state space feature vector. Standard prognostic health management metrics were used to quantify the performance of the algorithm against the actual remaining useful life. Application to part replacement decisions for ultra-high reliability system has been demonstrated. Using the technique described in the paper the appropriate time to reorder a replacement part could be monitored, and defended statistically. Robustness of the prognostication algorithm has been quantified using standard performance evaluation metrics.

PHM of Leadfree Interconnects Using Resistance Spectroscopy Based Particle Filter Models for Shock and Vibration Environments

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe ◽  
Kai Goebel
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Feature Vector ◽  
Health Management ◽  
Residual Life ◽  
The State ◽  
Remaining Useful Life ◽  
Damage Initiation ◽  
State Variable ◽  
Useful Life

Electronic assemblies have been monitored using state-space vectors from resistance spectroscopy, phase-sensitive detection and particle filtering (PF) to quantify damage initiation, progression and remaining useful life of the electronic assembly. A prognostication health management (PHM) methodology has been presented for electronic components subjected to mechanical shock and vibration. The presented methodology is an advancement of the state-of-art, which presently focuses on reactive failure detection and provides limited or no insight into the system reliability and residual life. Previously damage initiation, damage progression, and residual life in the pre-failure space has been correlated with micro-structural damage based proxies, feature vectors based on time, spectral and joint time-frequency characteristics of electronics [Lall2004a-d, 2005a-b, 2006a-f, 2007a-e, 2008a-f]. Precise resistance measurements based on the resistance spectroscopy method have been used to monitor interconnects for damage and prognosticate failure [Lall 2009a,b, 2010a,b, Constable 1992, 2001]. In this paper, the effectiveness of the proposed particle filter and resistance spectroscopy based approach in a prognostic health management (PHM) framework has been demonstrated for electronics. The measured state variable has been related to the underlying damage state using non-linear finite element analysis. The particle filter has been used to estimate the state variable, rate of change of the state variable, acceleration of the state variable and construct a feature vector. The estimated state-space parameters have been used to extrapolate the feature vector into the future and predict the time-to-failure at which the feature vector will cross the failure threshold. Remaining useful life has been calculated based on the evolution of the state space feature vector. Standard prognostic health management metrics were used to quantify the performance of the algorithm against the actual remaining useful life. Application to part replacement decisions for ultra-high reliability system has been demonstrated. Using the technique described in the paper the appropriate time to reorder a replacement part could be monitored, and defended statistically. Robustness of the prognostication algorithm has been quantified using standard performance evaluation metrics.

scholarly journals Remaining Useful Life Prediction of Bearings Using Ensemble Learning: The Impact of Diversity in Base Learners and Features

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Junchuan Shi ◽  
Tianyu Yu ◽  
Kai Goebel ◽  
Dazhong Wu
Keyword(s):  
Ensemble Learning ◽  
Health Management ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Methods ◽  
Vibration Data ◽  
Model Based ◽  
Useful Life ◽  
The Impact

Abstract Prognostics and health management (PHM) of bearings is crucial for reducing the risk of failure and the cost of maintenance for rotating machinery. Model-based prognostic methods develop closed-form mathematical models based on underlying physics. However, the physics of complex bearing failures under varying operating conditions is not well understood yet. To complement model-based prognostics, data-driven methods have been increasingly used to predict the remaining useful life (RUL) of bearings. As opposed to other machine learning methods, ensemble learning methods can achieve higher prediction accuracy by combining multiple learning algorithms of different types. The rationale behind ensemble learning is that higher performance can be achieved by combining base learners that overestimate and underestimate the RUL of bearings. However, building an effective ensemble remains a challenge. To address this issue, the impact of diversity in base learners and extracted features in different degradation stages on the performance of ensemble learning is investigated. The degradation process of bearings is classified into three stages, including normal wear, smooth wear, and severe wear, based on the root-mean-square (RMS) of vibration signals. To evaluate the impact of diversity on prediction performance, vibration data collected from rolling element bearings was used to train predictive models. Experimental results have shown that the performance of the proposed ensemble learning method is significantly improved by selecting diverse features and base learners in different degradation stages.

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.

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.

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