Statistical Pattern Recognition and Built-in Reliability Test for Feature Extraction and Health Monitoring of Electronics under Shock Loads

Lall, Pradeep; Choudhary, P.; Gupte, S.; Suhling, Jeff; Hofmeister, James

doi:10.1109/ectc.2007.373942

Cited by 69 publications

(5 citation statements)

References 73 publications

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“…3. Vibration combined with other downhole factors such as temperature, shock, and resonance have a damaging impact on drilling tool failure and lifetime [4,21,47,[66][67][68]. Table 2 lists research efforts on applying vibration factor in reliability modeling and predictions based on FRBD by statistical modeling, Bayesian method, data mining, Monte Carlo simulation, advanced computing, and analytics methods [17,69,70].…”

Section: Drill String Modeling Optimization Systemmentioning

confidence: 99%

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

Ren

Wang

Jiang

et al. 2018

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning.

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Section: Drill String Modeling Optimization Systemmentioning

confidence: 99%

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

Ren

Wang

Jiang

et al. 2018

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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“…Drilling electronics operate in extremely harsh downhole environments with temperatures exceeding 150C, shock and vibration levels exceeding 15g. The impact of temperature, shock and vibration on the life of electronics is described by Barker et al (1992), Duffek (2004), Garvey et al (2009), Gingerich et al (1999), Lall et al (2005Lall et al ( , 2007, Mirgkizoudi et al (2010), Pecht et al (1999), Vichare (2006), Vijayaragavan (2003), Wassell & Stroehlein (2010), White & Bernstein (2008). Other factors like power cycles, thermal ramp rates, electrical overstress, mechanical stress and manufacturing defects impact reliability of tools, but the factors cannot be accurately measured in downhole drilling environments and encompass high uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Material modeling to predict degradation of solder joints in the circuit board as results of thermo mechanical fatigue was developed by Nasser & Curtin (2006). Lall et al (2007) used experimental tests in combination with finite element analysis to model solder joint failure from shock and vibration. Mirgkizoudi et al (2010) developed a test plan to evaluate the reliability and service life of electronic components that are subject to a combination of mechanical, thermal, chemical or electrical inputs, and Wassell & Stroehlein (2010) use accelerated tests to derive accumulated damage models and failure thresholds as functions of vibration, shock levels, the number of shocks and the operating temperature.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Approach for Reliability and Life Prediction of Electronics in Drilling and Evaluation Tools

et al. 2014

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The capability to predict performance and lifetime of drilling electronics is the key to preventing costly downhole tool failures and ensuring success of any drilling operation. Drilling electronics operate under extremely harsh downhole environments with temperatures beyond 150C and vibration levels exceeding 15g. In addition to temperature and vibration, there are several factors affecting electronic reliability that have high uncertainty and cannot be accurately measured. There is a growing trend in the oil and gas industry to drill faster and operate at higher temperatures and pressures, forcing tools to operate beyond design specifications. This has resulted in increased failure rate leading to higher maintenance costs and system downtime for drilling operators as well as service providers. This paper develops a methodology to estimate the life of drilling electronics by using operational data, drilling dynamics and historical maintenance information. The methodology combines parameter estimation techniques, statistical reliability analysis and Bayesian math in a probabilistic framework. Parameter estimation is used to calibrate statistical equations to field data and probabilistic analysis is used to obtain the likelihood of failure. In the paper, the model parameters are represented as random variables, each with a probability distribution. Drilling electronics under downhole conditions can have several failure modes and each failure mode can be caused by the interaction of several variables. When information on each failure mechanism is not readily available, the failure is expressed in terms of several candidate models. Bayesian updating is used to incorporate real time operational history for a specific part and select the most accurate failure model for that part. Tis is for the first time, a systematic approach is developed for predicting the life of electronics in downhole drilling environments using statistical modeling and probabilistic methods on life cycle history and operational data from the field.

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“…These factors also support for comprehensive condition-based and reliability-centered maintenance. Prior to the introduction of a first SJ BIST TM sensor, there were no known methods available for detecting faults in solder-joint networks belonging to fully-programmed FPGAs, particularly FPGAs in Ball Grid Array (BGA) packages, such as XILINX® FG1156 [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Advanced testing and prognostics of Ball Grid Array (BGA) components with a stand-alone monitor IC

Bhatia

Hofmeister

Goodman

et al. 2009

2009 Ieee Autotestcon

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Ball Grid Array (BGA) packages have gained wide acceptance for use with FPGAs and the devices are used extensively for digital electronic designs. While these packages provide high interconnect densities, they rely upon an array of closely-spaced solder balls that are subject to cracking, oxidation and eventual failure. These solder joints can contribute to costly intermittencies that drive "No Fault Found" types of situations higher in the service depots. To overcome these intermittency problems, this paper presents a novel, stand-alone Integrated Circuit (IC) to be applied in systems for detection and isolation of intermittency faults in FPGAs. Because the detected faults are isolated before the FPGA begins to exhibit an intermittent failure, this provides more comprehensive approaches to supporting condition-based maintenance (CBM) and Enterprise Health Management (EHM) objectives for critical military/aerospace applications.

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Statistical Pattern Recognition and Built-in Reliability Test for Feature Extraction and Health Monitoring of Electronics under Shock Loads

Cited by 69 publications

References 73 publications

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

A Probabilistic Approach for Reliability and Life Prediction of Electronics in Drilling and Evaluation Tools

Advanced testing and prognostics of Ball Grid Array (BGA) components with a stand-alone monitor IC

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