Accrued Damage and Remaining Life in Field Extracted Assemblies Under Sequential Thermo-Mechanical Loads

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Mahendra Harsha ◽  
Jeff Suhling ◽  
Kai Goebel ◽  
Jim Jones
Keyword(s):  
Thermal Aging ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Relative Accuracy ◽  
Percentage Error ◽  
Valuable Insight ◽  
Sample Standard Deviation ◽  
Damage Initiation ◽  
Useful Life ◽  
Insight Into

Field deployed electronics may accrue damage due to environmental exposure and usage after finite period of service but may not often have any macro-indicators of failure such as cracks or delamination. A method to interrogate the damage state of field deployed electronics in the pre-failure space may allow insight into the damage initiation, progression, and remaining useful life of the deployed system. Aging has been previously shown to effect the reliability and constitutive behavior of second-level leadfree interconnects. Prognostication of accrued damage and assessment of residual life can provide valuable insight into impending failure. In this paper, field deployed parts have been extracted and prognosticated for accrued damage and remaining useful life in an anticipated future deployment environment. A subset of the field deployed parts have been tested to failure in the anticipated field deployed environment to validate the assessment of remaining useful life. In addition, some parts have been subjected to additional know thermo-mechanical stresses and the incremental damage accrued validated with respect to the amount of additional damage imposed on the assemblies. The presented methodology uses leading indicators of failure based on micro-structural evolution of damage to identify accrued damage in electronic systems subjected to sequential stresses of thermal aging and thermal cycling. Damage equivalency methodologies have been developed to map damage accrued in thermal aging to the reduction in thermo-mechanical cyclic life based on damage proxies. The expected error with interrogation of system state and assessment of residual life has been quantified. Prognostic metrics including α-λ metric, sample standard deviation, mean square error, mean absolute percentage error, average bias, relative accuracy, and cumulative relative accuracy have been used to compare the performance of the damage proxies.

Prognostic Health Monitoring of Damage Progression in Leadfree Electronics Under Sequential Exposure to Thermal Aging and Thermal Cycling

2010 ◽  
Author(s):  
Pradeep Lall ◽  
Rahul Vaidya ◽  
Vikrant More ◽  
Kai Goebel
Keyword(s):  
Thermal Cycling ◽  
Thermal Aging ◽  
Residual Life ◽  
High Reliability ◽  
Structural Evolution ◽  
Relative Accuracy ◽  
Percentage Error ◽  
Electronic Systems ◽  
Sample Standard Deviation ◽  
Reliability Systems

Deployed electronic systems may be subjected to cyclic thermo-mechanical loads during storage and subsequent to deployment. Aging has been previously shown to affect the reliability and constitutive behavior of second-level leadfree interconnects. Prognostication of accrued damage and assessment of residual life is extremely critical for ultra-high reliability systems in which the cost of failure is too high. The presented methodology uses leading indicators of failure based on micro-structural evolution of damage to identify impending failure in electronic systems subjected to sequential stresses of thermal aging and thermal cycling. The methodology has been demonstrated on area-array ball-grid array test assemblies with Sn3Ag0.5Cu interconnects subjected to thermal aging at 125°C and thermal cycling from −55 to 125°C for various lengths of time and cycles. Damage equivalency methodologies have been developed to map damage accrued in thermal aging to the reduction in thermo-mechanical cyclic life based on damage proxies. Prognostic metrics including α-λ metric, sample standard deviation, mean square error, mean absolute percentage error, average bias, relative accuracy, and cumulative relative accuracy have been used to compare the performance of the damage proxies.

Damage Pre-Cursors Based Prognostication of Accrued Damage and Assessment of Operational Readiness of Leadfree Electronics

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Mahendra Harsha ◽  
Jeff Suhling ◽  
Kai Goebel
Keyword(s):  
Thermal Cycling ◽  
Microstructural Evolution ◽  
Thermal Aging ◽  
Prolonged Exposure ◽  
Residual Life ◽  
High Reliability ◽  
Electronic Systems ◽  
Damage Initiation ◽  
Aging Conditions ◽  
Insight Into

Electronics in high reliability applications may be stored for extended periods of time prior to deployment. Prior studies have shown the elastic modulus and ultimate tensile strength of the SAC leadfree alloys reduces under prolonged exposure to high temperatures [Zhang 2009]. The thermal cycle magnitudes may vary over the lifetime of the product. Long-life systems may be re-deployed several times over the use life of the product. Previously, the authors have identified damage pre-cursors for correlation of the damage progression with the microstructural evolution of damage in second level interconnects [Lall 2004a-d, 2005a-b, 2006a-f, 2007a-e, 2008a-f, 2009a-d, 2010a-j]. Leadfree assemblies with Sn3Ag0.5Cu solder have been subjected to variety of thermal aging conditions including 60°C, 85°C and 125°C for periods of time between 1-week and 2-months, thermal cycling from −55°C to 125°C, −40°C to 95°C and 3°C to 100°C. The presented methodology uses leading indicators of failure based on microstructural evolution of damage to identify accrued damage in electronic systems subjected to sequential stresses of thermal aging and thermal cycling. Damage equivalency relationships have been developed to map damage accrued in thermal aging to the reduction in thermo-mechanical cyclic life based on damage proxies. Accrued damage between different thermal cyclic magnitudes has also been mapped for from −55°C to 125°C, −40°C to 95°C and 3°C to 100°C thermal cycles. The presented method for interrogation of the accrued damage for the field deployed electronics, significantly prior to failure, may allow insight into the damage initiation and progression of the deployed system. The expected error with interrogation of system state and assessment of residual life has been quantified.

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 Estimation of residual life of a cutting tool used in a machining process

2018 ◽  
Vol 192 ◽  
pp. 01017
Author(s):  
Sourath Ghosh ◽  
Sukanta Kumar Naskar ◽  
Nirmal Kumar Mandal
Keyword(s):  
Cutting Tool ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Cutting Condition ◽  
Remaining Life ◽  
Tool Failure ◽  
Life Data ◽  
Useful Life ◽  
Good Agreement

A significant part of cost of machining is associated with non-optimum use of cutting tool. Moreover cutting tool failure is responsible for almost 20% of the machining downtime. Thus, having knowledge of residual life of cutting tool is highly recommended so as to maximise the availability time and reduce the machining cost. The aim of this work is to find out residual life of a worn cutting tool which has been used for turning of Ti-6Al-4V alloy under constant cutting condition. The lognormal distribution is used to model the cutting tool life data. Remaining useful life of cutting tool is estimated using Mean Remaining Life (MRL) function. The results obtained from model are compared with the experimental results and it shows good agreement.

Residual life prediction for ball bearings based on joint approximate diagonalization of eigen matrices and extreme learning machine

2015 ◽  
Vol 231 (9) ◽  
pp. 1699-1711 ◽  
Author(s):  
Fang Liu ◽  
Yongbin Liu ◽  
Fenglin Chen ◽  
Bing He
Keyword(s):  
Frequency Domain ◽  
Extreme Learning Machine ◽  
Life Prediction ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Time Frequency ◽  
Fast Learning ◽  
Useful Life ◽  
Learning Machine ◽  
Degradation Indicator

Data-driven approaches have been proved effective for remaining useful life estimation of key components (bearings for example) in rotating machinery. In such approaches, it is important to determine an appropriate degradation indicator from the collected run-to-failure life cycle data. In this paper, a new degradation indicator is introduced based on the joint approximate diagonalization of eigen matrices algorithm. First, a matrix consisting of time domain, frequency domain, and time–frequency domain features extracted from the collected data instances is created. Then a two-layer joint approximate diagonalization of eigen matrices is introduced to transform the matrix to the advanced features (a vector) that represents the behavior of the bearing’s degradation. As an independent component analysis method, the designed two-layer joint approximate diagonalization of eigen matrices is able to eliminate the redundancy of the directly extracted features. Further, the obtained vector is input into an extreme learning machine to train a remaining useful life prediction model. Finally, a set of experimental cases are utilized to verify the presented method. Results show that the two-layer joint approximate diagonalization of eigen matrices is capable of exploring features that reflects the trend of bearing’s degradation state much better. And due to the easy parameter configuration and fast learning speed, the extreme learning machine is capable of training a model that can effectively predict the remaining useful life of the bearings.

scholarly journals Remaining Useful Life Prediction with Similarity Fusion of Multi-Parameter and Multi-Sample Based on the Vibration Signals of Diesel Generator Gearbox

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 861
Author(s):  
Shenghan Zhou ◽  
Xingxing Xu ◽  
Yiyong Xiao ◽  
Wenbing Chang ◽  
Silin Qian ◽  
...  
Keyword(s):  
Dynamic Programming Algorithm ◽  
Indicator System ◽  
Remaining Useful Life ◽  
Programming Algorithm ◽  
Percentage Error ◽  
Support Vector ◽  
Electrical Machine ◽  
Diesel Generator ◽  
Vibration Signals ◽  
Useful Life

The prediction of electrical machines’ Remaining Useful Life (RUL) can facilitate making electrical machine maintenance policies, which is important for improving their security and extending their life span. This paper proposes an RUL prediction model with similarity fusion of multi-parameter and multi-sample. Firstly, based on the time domain and frequency domain extraction of vibration signals, the performance damage indicator system of a gearbox is established to select the optimal damage indicators for RUL prediction. Low-pass filtering based on approximate entropy variance (Aev) is introduced in this process because of its stability. Secondly, this paper constructs Dynamic Time Warping Distance (DTWD) as a similarity measurement function, which belongs to the nonlinear dynamic programming algorithm. It performed better than the traditional Euclidean distance. Thirdly, based on DTWD, similarity fusion of multi-parameter and multi-sample methods is proposed here to achieve RUL prediction. Next, the performance evaluation indicator Q is adopted to evaluate the RUL prediction accuracy of different methods. Finally, the proposed method is verified by experiments, and the Multivariable Support Vector Machine (MSVM) and Principal Component Analysis (PCA) are introduced for comparative studies. The results show that the Mean Absolute Percentage Error (MAPE) of the similarity fusion of multi-parameter and multi-sample methods proposed here is below 14%, which is lower than MSVM’s and PCA’s. Additionally, the RUL prediction based on the DTWD function in multi-sample similarity fusion exhibits the best accuracy.

Leading Indicators for Prognostication of Impending Failures on Cu-Al Interconnects

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Shantanu Deshpande
Keyword(s):  
Thermal Aging ◽  
High Strength ◽  
Gold Wire ◽  
Wire Bonding ◽  
Remaining Useful Life ◽  
Wire Bond ◽  
Wire Bonds ◽  
Marquardt Algorithm ◽  
Useful Life ◽  
Copper Aluminum

Wire bonding is predominant mode of interconnect in electronics packaging. Traditionally material used for wire bonding is gold. But industry is slowly replacing gold wire bond by copper-aluminum wire bond because of the lower cost and better mechanical properties than gold, such as high strength, high thermal conductivity etc. Numerous studies have been done to analyze failure mechanism of Cu-Al wire bonds. Cu-Al interface is a predominant location for failure of the wirebond interconnects. In this paper, the use of intermetallic thickness as leading indicator-of-failure for prognostication of remaining useful life for Cu-Al wire bond interconnects has been studied. For analysis, 32 pin chip scale packages were used. Packages were aged isothermally at 200°C and 250°C for 10 days. Packages were withdrawn periodically after 24 hours and its IMC thickness was measured using SEM. The parts have been prognosticated for accrued damage and remaining useful life in current or anticipated future deployment environment. The presented methodology uses evolution of the IMC thickness in conjunction with the Levenberg-Marquardt Algorithm to identify accrued damage in wire bond subjected to thermal aging. The proposed method can be used for equivalency of damage accrued in Cu-Al parts subjected to multiple thermal aging environments.

scholarly journals A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

2021 ◽  
Author(s):  
Zhao-Hua Liu ◽  
Xu-Dong Meng ◽  
Hua-Liang Wei ◽  
Liang Chen ◽  
Bi-Liang Lu ◽  
...  
Keyword(s):  
Residual Life ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Elastic Net ◽  
Maintenance Cost ◽  
Real World Data ◽  
Rolling Bearings ◽  
Safety And Reliability ◽  
Useful Life

AbstractRotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life (RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network (LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure. In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

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