Comparison of Prognostic Health Management Algorithms for Assessment of Electronic Interconnect Reliability

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe ◽  
Kai Goebel
Keyword(s):  
Kalman Filter ◽  
Printed Circuit Board ◽  
Health Management ◽  
Remaining Useful Life ◽  
Circuit Board ◽  
Data Set ◽  
Printed Circuit ◽  
Interconnect Reliability ◽  
Useful Life ◽  
Management Algorithms

This paper compares three prognostic algorithms applied to the same data recorded during the failure of a solder joint in ball grid array component attached to a printed circuit board. The objective is to expand on the relative strengths and weaknesses of each proposed algorithm. Emphasis will be placed on highlighting differences in underlying assumptions required for each algorithm, details of remaining useful life calculations, and methods of uncertainty quantification. Metrics tailored specifically for Prognostic Health Monitoring (PHM) are presented to characterize the performance of predictions. The relative merits of PHM algorithms based on a Kalman filter, extended Kalman filter, and a particle filter all demonstrated on the same data set will be discussed. The paper concludes by discussing which algorithm performs best given the information available about the system being monitored.

Comparison of Prognostic Health Management Algorithms for Assessment of Electronic Interconnect Reliability Under Vibration

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe
Keyword(s):  
Kalman Filter ◽  
Printed Circuit Board ◽  
Health Management ◽  
Remaining Useful Life ◽  
Circuit Board ◽  
Data Set ◽  
Printed Circuit ◽  
Interconnect Reliability ◽  
Useful Life ◽  
Management Algorithms

This paper compares three prognostic algorithms applied to the same data recorded during the failure of a solder joint in ball grid array component attached to a printed circuit board. The objective is to expand on the relative strengths and weaknesses of each proposed algorithm. Emphasis will be placed on highlighting differences in underlying assumptions required for each algorithm, details of remaining useful life calculations, and methods of uncertainty quantification. Metrics tailored specifically for prognostic health monitoring (PHM) are presented to characterize the performance of predictions. The relative merits of PHM algorithms based on a Kalman filter, extended Kalman filter, and a particle filter all demonstrated on the same data set will be discussed. The paper concludes by discussing which algorithm performs best given the information available about the system being monitored.

scholarly journals A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

2019 ◽  
Vol 9 (3) ◽  
pp. 613
Author(s):  
Bangcheng Zhang ◽  
Yubo Shao ◽  
Zhenchen Chang ◽  
Zhongbo Sun ◽  
Yuankun Sui
Keyword(s):  
Kalman Filter ◽  
Health Management ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Time Prediction ◽  
Model Based ◽  
Stochastic Parameters ◽  
Deterioration Process ◽  
Useful Life ◽  
Posterior Estimation

Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

Prognosis Methodologies for Health Management of Electronics and MEMS Packaging

2004 ◽  
Author(s):  
Pradeep Lall ◽  
Nokibul Islam ◽  
Kaysar Rahim ◽  
Jeff Suhling
Keyword(s):  
Printed Circuit Board ◽  
Prediction Models ◽  
Health Management ◽  
Residual Life ◽  
Structural Evolution ◽  
Remaining Useful Life ◽  
Circuit Board ◽  
Leading Indicators ◽  
Phase Size ◽  
Material State

The current state-of-art in managing system reliability is geared towards the development of life-prediction models for unaged pristine materials under known loading conditions based on relationships such as the Paris’s Power Law [Paris, et. al 1960, 1961], Coffin-Manson Relationship [Coffin 1954; Tavernelli, et. al. 1959; Smith, et. al. 1964; Manson, et. al. 1964] and the S-N Diagram. There is need for methods and processes which will allow interrogation of complex systems and sub-systems to determine the remaining useful life prior to repair or replacement. This capability of determination of material or system state is called “prognosis”. In this paper, a methodology for prognosis-of-electronics has been demonstrated with data of leading indicators of failure for accurate assessment of product damage significantly prior to appearance of any macro-indicators of damage. Proxies for leading indicators of failure have been developed including – micro-structural evolution characterized by average phase size and interfacial stresses at interface of silicon structures. Structures examined include – electronics package, MEMS Packages and interconnections on a metal backed printed circuit board typical of electronics deployed in harsh environments. Since, an aged material knows its state the research presented in this paper focuses on enhancing the understanding of material damage to facilitate proper interrogation of material state. Mathematical relationship has been developed between phase growth rate and time-to-1-percent failure to enable the computation of damage manifested and a forward estimate of residual life.

scholarly journals The PV Corrosion Fault Prognosis Based on Ensemble Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Radouane Ouladsine ◽  
Rachid Outbib ◽  
Mohamed Bakhouya
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Climatic Conditions ◽  
Measured Data ◽  
Remaining Useful Life ◽  
Data Set ◽  
Pv Modules ◽  
Fault Prognosis ◽  
Useful Life ◽  
Degradation Modes

The degradation of photovoltaic (PV) modules remains a major concern on the control and the development of the photovoltaic field, particularly, in regions with difficult climatic conditions. The main degradation modes of the PV modules are corrosion, discoloration, glass breaks, and cracks of cells. However, corrosion and discoloration remain the predominant degradation modes that still require further investigations. In this paper, a model-based PV corrosion prognostic approach, based on an ensemble Kalman filter (EnKF), is introduced to identify the PV corrosion parameters and then estimate the remaining useful life (RUL). Simulations have been conducted using measured data set, and results are reported to show the efficiency of the proposed approach.

scholarly journals A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 176 ◽  
Author(s):  
David Verstraete ◽  
Enrique Droguett ◽  
Mohammad Modarres
Keyword(s):  
Sensor Fusion ◽  
Asset Management ◽  
Life Prediction ◽  
Health Management ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Useful Life

Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.

scholarly journals Remaining Useful Life Estimation of Aircraft Engines Based on Deep Convolution Neural Network and LightGBM Combination Model

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Lijun Liu ◽  
Lan Wang ◽  
Zhen Yu
Keyword(s):  
Time Window ◽  
Health Management ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Data Set ◽  
Light Gradient ◽  
Model Combining ◽  
Aero Engine ◽  
Useful Life

AbstractAccurately predicting the remaining useful life (RUL) of aero-engines is of great significance for improving the reliability and safety of aero-engine systems. Because of the high dimension and complex features of sensor data in RUL prediction, this paper proposes a model combining deep convolution neural networks (DCNN) and the light gradient boosting machine (LightGBM) algorithm to estimate the RUL. Compared with traditional prognostics and health management (PHM) techniques, signal processing of raw sensor data and prior expertise are not required. The procedure is shown as follows. First, the time window of raw data of the aero-engine is used as the input of DCNN after normalization. The role of DCNN is to extract information from the input data. Second, considering the limitations of the fully connected layer of DCNN, we replace it with a strong classifier-LightGBM to improve the accuracy of prediction. Finally, to prove the effectiveness of the proposed method, we conducted some experiments on the C-MAPSS data set provided by NASA, and obtained good accuracy. By comparing the prediction effect with other commonly used algorithms on the same data set, the proposed algorithm has obvious advantages.

Characteristics of Radiated Emission from a Printed Circuit Board with a Slit

2012 ◽  
Vol 132 (6) ◽  
pp. 404-410 ◽  
Author(s):  
Kenichi Nakayama ◽  
Kenichi Kagoshima ◽  
Shigeki Takeda
Keyword(s):  
Printed Circuit Board ◽  
Circuit Board ◽  
Printed Circuit ◽  
Radiated Emission

Confirming the Signal Integrity in Transmission of Digital Signals on Microstrip Straight Circuits via the Eye Diagrams

2014 ◽  
Vol 5 (1) ◽  
pp. 737-741
Author(s):  
Alejandro Dueñas Jiménez ◽  
Francisco Jiménez Hernández
Keyword(s):  
Integrated Circuits ◽  
Printed Circuit Board ◽  
Signal Integrity ◽  
High Volume ◽  
Information Storage ◽  
Circuit Board ◽  
Electromagnetic Simulation ◽  
Electronic Systems ◽  
Digital Signals ◽  
Printed Circuit

Because of the high volume of processing, transmission, and information storage, electronic systems presently requires faster clock speeds tosynchronizethe integrated circuits. Presently the “speeds” on the connections of a printed circuit board (PCB) are in the order of the GHz. At these frequencies the behavior of the interconnects are more like that of a transmission line, and hence distortion, delay, and phase shift- effects caused by phenomena like cross talk, ringing and over shot are present and may be undesirable for the performance of a circuit or system.Some of these phrases were extracted from the chapter eight of book “2-D Electromagnetic Simulation of Passive Microstrip Circuits” from the corresponding author of this paper.

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