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Journal articles on the topic 'Failure identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Khoso, Ali Raza, Jam Shahzaib Khan, Rizwan Ullah Faiz, Muhammad Akram Akhund, Aftab Ahmed, and Faizullah Memon. "Identification of Building Failure Indicators." Engineering, Technology & Applied Science Research 9, no. 5 (2019): 4591–95. https://doi.org/10.5281/zenodo.3510125.

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A most frequent problem in the construction industry of Pakistan is the failure of structures, especially buildings, before their expected design life. Structural failure in buildings at the time of construction or after completion may result in human casualties, waste of money, social disturbance, and clashes, and claims between stakeholders. Many researches have already been conducted about key causes which lead a structure to fail at later stages. This research is pursuing to analyze early building failure indicators (BFIs) in Pakistan. Indicators and causes emerged from a thorough literatu

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Khoso, A. R., J. S. Khan, R. U. Faiz, M. A. Akhund, A. Ahmed, and F. Memon. "Identification of Building Failure Indicators." Engineering, Technology & Applied Science Research 9, no. 5 (2019): 4591–95. http://dx.doi.org/10.48084/etasr.2872.

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A most frequent problem in the construction industry of Pakistan is the failure of structures, especially buildings, before their expected design life. Structural failure in buildings at the time of construction or after completion may result in human casualties, waste of money, social disturbance, and clashes, and claims between stakeholders. Many researches have already been conducted about key causes which lead a structure to fail at later stages. This research is pursuing to analyze early building failure indicators (BFIs) in Pakistan. Indicators and causes emerged from a thorough literatu

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3

Gupta, R. P., R. D. Gupta, and P. G. Sankaran. "Identification of Failure Rates." Calcutta Statistical Association Bulletin 54, no. 3-4 (2003): 209–22. http://dx.doi.org/10.1177/0008068320030306.

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4

Massoumnia, M. A., G. C. Verghese, and A. S. Willsky. "Failure detection and identification." IEEE Transactions on Automatic Control 34, no. 3 (1989): 316–21. http://dx.doi.org/10.1109/9.16422.

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5

Agostini, Marisa. "Two common steps in firms’ failing path." Risk Governance and Control: Financial Markets and Institutions 3, no. 1 (2013): 108–21. http://dx.doi.org/10.22495/rgcv3i1c1art5.

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This paper aims to identify two steps which are common to the path of all failing firms and result from their financial statements. Their identification support the explanation of business failure (in both fraud and no-tort cases) as encouraged by authoritative literature (Cybinski, 2001; Parker, 2012). The analysis has been conducted through all the fraud cases (and the matched not-tort cases) mentioned by WebBRD. It has been developed through different phases: content analysis for the identification and categorization of micro-failures, a deep analysis of time variable and the implementation

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6

Liu, Jiqun, and Chirag Shah. "Proactive identification of query failure." Proceedings of the Association for Information Science and Technology 56, no. 1 (2019): 176–85. http://dx.doi.org/10.1002/pra2.15.

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Zhang, Bing, Zhaojun Hao, Lifang Wang, and Lexiao Li. "Key Failure Mode Identification Method of LED System Based on Fuzzy TOPSIS." Journal of Physics: Conference Series 2489, no. 1 (2023): 012024. http://dx.doi.org/10.1088/1742-6596/2489/1/012024.

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Abstract The premise of the system reliability analysis is to effectively discern the patterns of product failures at all levels. To deal with the problem that the diversity of failure modes of complex products represented by LED systems cannot be analyzed comprehensively, in this paper, a key failure mode identification method based on Fuzzy TOPSIS is proposed. First, the construction method of the failure relevance tree is given to support the comprehensive identification of potential failure modes. Then, the decision rule of node failure weight is mined by the rough set method, the systemat

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Cheng, Hang, Xi Li, Zheng Bo Qin, and Chao Yong Huang. "Method of Bearing Fault Identification Based on SVM Decision Tree." Applied Mechanics and Materials 190-191 (July 2012): 1010–15. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.1010.

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This paper has put forward one method, combining with theory of decision tree and method of voting, and established one kind of multi-failure classifier of SVM, which could finish cross training, repeated classification and accumulative voting. This classifier could accomplish classification of failure bearings and do classification experiments among failures of kinds of bearings. The results have shown that this method could identify states of breakdown equipment for the purpose of diagnosis of failures of mechanical system accurately.

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He, Qing Chuan, Wen Hua Chen, Jun Pan, and Shi Jiao Wang. "Challenges in Reliability Assessment for Electronics." Advanced Materials Research 118-120 (June 2010): 419–23. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.419.

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There has been a growing interest in assessing the ongoing reliability of electronics and systems in order to predict failures and provide warning to avoid catastrophic failure. Methods based on prognostics and health management shows an enabling technology to assess the reliability of electronics and systems under its actual application conditions. However, many challenges in implementation of methods based on PHM still remain including: environmental and usage profiles for life-cycle loads, identification of failure mechanism, identification of failure PoF model, identification of parameters

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Nikhil, Chitturi Sai, Manjula Josephine Bollarapu, Kaja Sai Sree Neeraj, Alaparthi Praveen Kumar, and Manchikalapudi Jaya Sesha Sai Raghuveer. "Efficient identification of node failure and recovery through end to end Probing techniques." Journal of Physics: Conference Series 2040, no. 1 (2021): 012006. http://dx.doi.org/10.1088/1742-6596/2040/1/012006.

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Abstract Identification of Node failure detection and a localization is a very important challenge in a network community to get a quick recovery and avoid useless traffic in network. But it is very difficult to check the failure nodes or locations because of the large number of Screw ups in dense network. As finding the main source for failure of network is always challenging our proposed work will achieve that, it identifies the node failure by using probing measurement of binary state to end to end paths. Apart from identifying the network failure, it also quantifies the total failure nodes

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11

Szpak, Dawid, and Izabela Piegdoń. "Identification of Failure Causes in the Water Supply Network." Journal of KONBiN 49, no. 2 (2019): 283–300. http://dx.doi.org/10.2478/jok-2019-0036.

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Abstract Water supply network is the most likely element which can fail in the whole water supply system. Failures of the water supply network often results in limitation or lack of water supply to the consumers. Identification and ranking of failure causes in terms of frequency, are the possibility to present proposals of action, related to the replacement or modernization of the water supply network. In addition, the water supply network failures were related to the main parameter important for the water consumers the time of suspended supplying in water.

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NICOLIN, Ilie, and Bogdan Adrian NICOLIN. "Failure Mode and Effect Analysis for a military nose landing gear project." INCAS BULLETIN 13, no. 4 (2021): 205–12. http://dx.doi.org/10.13111/2066-8201.2021.13.4.17.

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Failure Mode and Effect Analysis (FMEA) techniques were originally developed by the US Military and have been used as techniques for assessing the reliability and effects of equipment failures. However, the first notable applications of FMEA techniques are related to the impressive development of the aerospace industry in the mid-1960s. FMEA is a methodology for systematically analyzing the failure modes of a project, product or process, prioritizing their importance, identifying system failure mechanisms, analyzing potential failure modes and the effects of these failures, followed by correct

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13

Zhmatov, Dmitry. "Failure risk identification in power transformers." Energy Safety and Energy Economy 6 (December 2017): 47–49. http://dx.doi.org/10.18635/2071-2219-2017-6-47-49.

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Zhang, Zan, Weiguo Liu, Jichang Peng, et al. "Identification of TBAES rotating diode failure." IET Electric Power Applications 11, no. 2 (2017): 260–71. http://dx.doi.org/10.1049/iet-epa.2016.0468.

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15

Nafday, Avinash M., Ross B. Corotis, and Jared L. Cohort. "Failure Mode Identification for Structural Frames." Journal of Structural Engineering 113, no. 7 (1987): 1415–32. http://dx.doi.org/10.1061/(asce)0733-9445(1987)113:7(1415).

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16

Ioannou, S., E. Stefanakos, and P. H. Wiley. "New MOV Failure Mode Identification Invention." IEEE Transactions on Consumer Electronics 53, no. 3 (2007): 1068–75. http://dx.doi.org/10.1109/tce.2007.4341587.

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17

Liu, Yamei, C. P. Diduch, and R. Doraiswami. "Modeling and identification for failure diagnosis." IFAC Proceedings Volumes 36, no. 5 (2003): 381–86. http://dx.doi.org/10.1016/s1474-6670(17)36521-7.

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18

Kohda, T., and K. Inoue. "Failure Identification Procedure using Redundant Rules." IFAC Proceedings Volumes 25, no. 9 (1992): 71–76. http://dx.doi.org/10.1016/s1474-6670(17)50172-x.

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19

Maleki, Sepehr, Paolo Rapisarda, Lorenzo Ntogramatzidis, and Eric Rogers. "Failure identification for 3D linear systems." Multidimensional Systems and Signal Processing 26, no. 2 (2013): 481–502. http://dx.doi.org/10.1007/s11045-013-0271-2.

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Maleki, Sepehr, Paolo Rapisarda, and Eric Rogers. "Failure identification for linear repetitive processes." Multidimensional Systems and Signal Processing 26, no. 4 (2015): 1037–59. http://dx.doi.org/10.1007/s11045-015-0345-4.

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21

Arunajadai, Srikesh G., Scott J. Uder, Robert B. Stone, and Irem Y. Tumer. "Failure Mode Identification through Clustering Analysis." Quality and Reliability Engineering International 20, no. 5 (2004): 511–26. http://dx.doi.org/10.1002/qre.663.

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22

White, Halbert, and Karim Chalak. "Identification and Identification Failure for Treatment Effects Using Structural Systems." Econometric Reviews 32, no. 3 (2013): 273–317. http://dx.doi.org/10.1080/07474938.2012.690664.

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23

Emerson, Rachel M., Nepu Saha, Pralhad H. Burli, Jordan L. Klinger, Tiasha Bhattacharjee, and Lorenzo Vega-Montoto. "Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability." Energies 17, no. 11 (2024): 2516. http://dx.doi.org/10.3390/en17112516.

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This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outpu

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24

Gabriel, S. K. Akakpo, Kwame Eyram, S. A. Gladstone-Bright P., and S. Ewedji Christabel. "Identification of Risks of Failure of Pneumatic Tyres of Passenger Vehicles in Ghana." International Journal of Social Science and Humanities Research 10, no. 2 (2022): 170–81. https://doi.org/10.5281/zenodo.6542318.

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<strong>Abstract:</strong> In-transit failures of pneumatic tyres of heavy vehicles such as passenger buses, which ply some major intercity highways in Ghana remain a great source of worry in Ghanaian transportation system. The study seeks to identify major causes of the in-transit failures of pneumatic tyres of heavy vehicles, and how to prevent such failures. Five thousand (5000) pneumatic tyres were sampled using a simple random sampling method and idiosyncrasies of some drivers of these heavy passenger buses were observed. The data gathered was analyzed using cross tabulation and simple st

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25

Ballas, James A., Kristin N. Dick, and Mary R. Groshek. "Failure to Identify “Identifiable” Sounds." Proceedings of the Human Factors Society Annual Meeting 31, no. 2 (1987): 144–46. http://dx.doi.org/10.1177/154193128703100201.

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The identifiability of sounds used in the Meaningful Sounds Identification test was measured. Identifiability was quantified with a measure of identification uncertainty. Calculation of this measure involves sorting the identification responses into categories of similar causes. Two exemplar sets of the sounds were used and the response data were analyzed separately by two sorters. All of the sounds were not highly identifiable, as was expected given the use of the test. The results were comparable for the two set of exemplars, and for the two sorters. The results demonstrate the importance of

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26

Li, Linzhi, Lu Liu, Hao Wu, Yonghua Song, Dunwen Song, and Yi Liu. "Identification of Critical Hidden Failure Line Based on State-failure-network." Journal of Modern Power Systems and Clean Energy 10, no. 1 (2022): 40–49. http://dx.doi.org/10.35833/mpce.2020.000056.

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27

Dyorina, N. V., V. S. Velikanov, and K. S. Dyorina. "Identification of failure patterns of excavator equipment failures considering the control factor." IOP Conference Series: Materials Science and Engineering 971 (December 1, 2020): 042062. http://dx.doi.org/10.1088/1757-899x/971/4/042062.

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28

Hu, Hangyu, Fan Wu, Xiaowei Xie, Qiang Wei, Xuemeng Zhai, and Guangmin Hu. "Critical node identification in network cascading failure based on load percolation." Electronic Research Archive 31, no. 3 (2023): 1524–42. http://dx.doi.org/10.3934/era.2023077.

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<abstract> <p>Identification of network vulnerability is one of the important means of cyberspace operation, management and security. As a typical case of network vulnerability, network cascading failures are often found in infrastructure networks such as the power grid system, communication network and road traffic, where the failure of a few nodes may cause devastating disasters to the whole complex system. Therefore, it is very important to identify the critical nodes in the network cascading failure and understand the internal laws of cascading failure in complex systems so as

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29

Ni, Lingli, and Chris R. Fuller. "Control Reconfiguration Based on Hierarchical Fault Detection and Identification for Unmanned Underwater Vehicles." Journal of Vibration and Control 9, no. 7 (2003): 735–48. http://dx.doi.org/10.1177/1077546303009007001.

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Unmanned underwater vehicles (UUVs) have been developed for various applications in ocean engineering. When failures occur to UUVs and result in abnormal operations, the only solution is to abort from the mission due to lack of fault tolerance. The purpose of this study is to investigate a method by which UUVs can continue to operate acceptably following failure occurrences. Based on a unique hierarchical fault detection and identification, this paper presents a control reconfiguration scheme with multiple sliding-mode controllers for each of the hypothesized failure modes from a discrete set

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30

Tchórzewska-Cieślak, Barbara, and Izabela Piegdoń. "The Method of Identification the Failure Risk on Water Supply Networks." Journal of KONBiN 37, no. 1 (2016): 73–94. http://dx.doi.org/10.1515/jok-2016-0004.

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Abstract Daily exploitation of the water network, collecting of data concerning the failure of water pipes and knowledge of the repair team are the basis of the data used in the process of effectively removing and identifying failures. The paper proposes a methodology to identify the risk of failure of the water supply system that uses risk indexes according to the proposed three-stage scale of risk levels. An example of application methods for the actual operational data broken down by analysis and risk assessment for bus cables and a separate analysis for the distribution network.

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Rashedi, Reza, and Fred Moses. "Identification of Failure Modes in System Reliability." Journal of Structural Engineering 114, no. 2 (1988): 292–313. http://dx.doi.org/10.1061/(asce)0733-9445(1988)114:2(292).

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32

Yani, Irsyadi, Yulia Resti, and Firmansyah Burlian. "Identification of Bearing Failure Using Signal Vibrations." Journal of Physics: Conference Series 1007 (April 2018): 012067. http://dx.doi.org/10.1088/1742-6596/1007/1/012067.

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33

Sze, Shirley, Pierpaolo Pellicori, Jufen Zhang, Joan Weston, and Andrew L. Clark. "Identification of Frailty in Chronic Heart Failure." JACC: Heart Failure 7, no. 4 (2019): 291–302. http://dx.doi.org/10.1016/j.jchf.2018.11.017.

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34

Brehm, Eric, and Robert Hertle. "Failure Identification: Procedural Causes and Corresponding Responsibilities." Structural Engineering International 27, no. 3 (2017): 402–8. http://dx.doi.org/10.2749/101686617x14881937385160.

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35

Trebuňa, F., F. Šimčák, J. Bocko, and P. Trebuňa. "Identification of causes of radial fan failure." Engineering Failure Analysis 16, no. 7 (2009): 2054–65. http://dx.doi.org/10.1016/j.engfailanal.2009.01.007.

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36

Forneron, Jean-Jacques. "Detecting identification failure in moment condition models." Journal of Econometrics 238, no. 1 (2024): 105552. http://dx.doi.org/10.1016/j.jeconom.2023.105552.

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37

Mileski, Michael, Rebecca McClay, and Jessica Natividad. "Facilitating Factors in the Proper Identification of Acute Skin Failure: A Systematic Review." Critical Care Nurse 41, no. 2 (2021): 36–42. http://dx.doi.org/10.4037/ccn2021145.

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Background Acute skin failure is a significant medical finding for both the critical care patient and the hospital. Proper identification is key to prevention and treatment, but diagnosis of acute skin failure in critical care patients is often missed. This diagnostic oversight may be due to a lack of knowledge about acute skin failure and its presentation. Objective To investigate the literature for facilitating factors that would ease the identification of acute skin failure for clinicians. Methods The research team conducted a systematic literature review via PubMed, CINAHL, and Academic Se

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38

Chen, Taizhong. "Research on the design of non-invasive load monitoring system." Applied and Computational Engineering 55, no. 1 (2024): 81–86. http://dx.doi.org/10.54254/2755-2721/55/20241663.

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In industrial production, machine failure will cause production efficiency decline, maintenance costs increase and other problems, while the traditional manual maintenance can only be carried out after the failure, cannot play an early warning effect, and has low efficiency. Non-invasive load identification and condition estimation can predict upcoming failures, remind maintenance in time, reduce failures, and improve production efficiency. In this paper, a non-intrusive load monitoring system is designed based on a non-contact high-frequency synchronous power data acquisition device. A non-in

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Winkler, Sheldon. "Extraordinary Implant Failure." Journal of Oral Implantology 36, no. 5 (2010): 391–400. http://dx.doi.org/10.1563/aaid-joi-d-09-00088.

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Abstract Every attempt must be made to keep implant failures to a minimum. Dental implant failure can roughly be divided into 3 broad categories: bacterial factors, occlusal (mechanical factors), and systemic and psychologic factors. All failures should be carefully analyzed and evaluated to identify their causes in order to prevent future reoccurrence. Failures should be documented and used to advantage when treatment planning future implant cases. A panoramic radiograph, mounted diagnostic casts, medical and dental histories, and surgical guides are accepted standards of care prior to beginn

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Salmanov, Khalid, and Hadi Harb. "Data Analysis for the Aero Derivative Engines Bleed System Failure Identification and Prediction." International Journal of Intelligent Systems and Applications 13, no. 6 (2021): 13–24. http://dx.doi.org/10.5815/ijisa.2021.06.02.

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Middle size gas/diesel aero-derivative power generation engines are widely used on various industrial plants in the oil and gas industry. Bleed of Valve (BOV) system failure is one of the failure mechanisms of these engines. The BOV is part of the critical anti-surge system and this kind of failure is almost impossible to identify while the engine is in operation. If the engine operates with BOV system impaired, this leads to the high maintenance cost during overhaul, increased emission rate, fuel consumption and loss in the efficiency. This paper proposes the use of readily available sensor d

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Wang, Mei Qing, and Bin Wang. "Key Elemental Function Identification Based on Function-Failure Knowledge." Applied Mechanics and Materials 58-60 (June 2011): 1435–41. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1435.

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Functional modeling is the key step in product design process. To improve the quality of functional modeling, an identification method of key elemental function based on function-failure knowledge was proposed. A function-failure knowledge model for mechanical product was built, which consists of function-failure mode, relationships among failure modes, and failure risk information. By means of the function-failure repository, the approach to calculate risk number (RN) of the elemental function was presented, in which the relationships among failure modes were considered. Then the key elementa

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42

Tian, Yulin, Xiaoming Liu, Zhen Li, Shaohu Tang, Chunlin Shang, and Lu Wei. "Identification of Critical Links in Urban Road Network considering Cascading Failures." Mathematical Problems in Engineering 2021 (April 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/9994347.

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The identification of critical road links is greatly important to the management and control of the transportation system. Existing works fail to fully consider the influence of the distribution of traffic flow and its dynamic characteristics on critical road link identification. In this paper, we propose a criticality calculation method for urban road networks considering the effect of cascading failures which models the distribution change of traffic flow after a specific road link failed. Firstly, a sequence diagram calculation method is proposed to model how the traffic failure on one road

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Nasser, Reem, and Yiliu Liu. "Enhancing reliability and safety in standalone or hybrid hydroelectric-wind energy systems: Failure events and safety barriers." E3S Web of Conferences 636 (2025): 04005. https://doi.org/10.1051/e3sconf/202563604005.

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The reliability and safety of low-carbon energy systems, particularly hydroelectric and wind energy, are critical concerns given their increasing adoption and inherent failure risks. This study examines failure events in standalone and hybrid hydroelectric-wind systems, emphasizing the role of safety barriers in mitigating hazards. Failures in these systems can be categorized as detectable or undetectable, with undetectable failures posing significant risks due to delayed identification and intervention. The detectability of failures in each scenario has been assessed based on the difficulty a

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Li, He, Weiwen Peng, Cheng-Geng Huang, and C. Guedes Soares. "Failure Rate Assessment for Onshore and Floating Offshore Wind Turbines." Journal of Marine Science and Engineering 10, no. 12 (2022): 1965. http://dx.doi.org/10.3390/jmse10121965.

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A detailed analysis is performed on a dataset of failure and maintenance records from various onshore wind farms located in different geographical areas for the safety, risk, reliability, availability, and maintainability characterization of wind turbines. Specifically, characteristics related to failures, including the criticality of failure modes, failure frequencies, failure rates, and lifetime distributions of components, are analyzed to support the failure identification and failure prevention of wind turbines. Additionally, characteristics of maintenance, including typical maintenance me

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45

Ryals, Anthony J., Carly A. Yadon, Jason S. Nomi, and Anne M. Cleary. "When word identification fails: ERP correlates of recognition without identification and of word identification failure." Neuropsychologia 49, no. 12 (2011): 3224–37. http://dx.doi.org/10.1016/j.neuropsychologia.2011.07.027.

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46

Ma, Jiming, and Jianbin Guo. "Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/361631.

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The diagnosis procedure is performed by integrating three steps: multidomain modeling, event identification, and failure event classification. Multidomain model can describe the normal and fault behaviors of hybrid systems efficiently and can meet the diagnosis requirements of hybrid systems. Then the multidomain model is used to simulate and obtain responses under different failure events; the responses are further utilized as a priori information when training the event identification library. Finally, a brushless DC motor is selected as the study case. The experimental result indicates that

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Zuber, Ninoslav, and Dragan Cvetkovic. "Rolling Element Bearings Fault Identification in Rotating Machines, Existing Methods of Vibration Signal Processing Techniques and Practical Considerations." Applied Mechanics and Materials 430 (September 2013): 70–77. http://dx.doi.org/10.4028/www.scientific.net/amm.430.70.

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This paper addresses the suitability of vibration monitoring and analysis techniques to detect different types of defects in roller element bearings. Processing techniques are demonstrated on signals acquired from the test rig with defective bearings. As a result it is shown that there is no reliable universal method for bearing failure monitoring from its early occurence up to bearings failure. Two real life case studies with different types of bearing failures are presented with practical considerations on methods used for failure identification.

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48

Zhu, Yantao, Xinqiang Niu, Chongshi Gu, Dashan Yang, Qiang Sun, and E. Fernandez Rodriguez. "Using the DEMATEL-VIKOR Method in Dam Failure Path Identification." International Journal of Environmental Research and Public Health 17, no. 5 (2020): 1480. http://dx.doi.org/10.3390/ijerph17051480.

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Dams are important water-resisting structures prone to failure, causing huge economic and environmental losses. Traditionally, a dam failure is identified using the failure mode and effect analysis. This approach analyzes both the dam failure path (the specific effect chain of the failure mode) and the damage degree, by identifying and sorting the severity caused by the dam failure path. However, this analysis can be misleading since the relationship among the failure paths is not considered. To account for this, the DEMATEL method is used to modify the evaluation result of the severity of the

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Hu, J., D. Barker, A. Dasgupta, and A. Arora. "Role of Failure-Mechanism Identification in Accelerated Testing." Journal of the IEST 36, no. 4 (1993): 39–45. http://dx.doi.org/10.17764/jiet.2.36.4.b01608702h803nkm.

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Accelerated life testing techniques provide a short-cut method to investigate the reliability of electronic devices with respect to certain dominant failure mechanisms that occur under normal operating conditions. However, accelerated tests have often been conducted without knowledge of the failure mechanisms and without ensuring that the test accelerated the same mechanism as that obscrved under normal operating conditions. This paper summarizes common failure mechanisms in electronic devices and packages and investigates possible failure mechanism shifting during accelerated testing.

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Gofar, Nurly, Yudi Haryanto, and Anggi Purnama Sari Dewi. "Identification of Rainfall Scenario Triggering Slope Failures in Pagar Alam and its Surrounding Area." MEDIA KOMUNIKASI TEKNIK SIPIL 30, no. 2 (2025): 256–64. https://doi.org/10.14710/mkts.v30i2.62372.

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Abstract:

Pagar Alam and its surrounding area are located in the Bukit Barisan Mountain Rang, thus, the topography is hilly and mountainous. National Road segments passing the area often experience slope failure, which causes distraction to the transportation of people and goods. Past observations showed that slope failures are related to frequent and prolonged rainfall. This paper presents the results of a numerical study on the mechanism of rainfall-induced slope failures in Pagar Alam. The study aimed to identify the critical rainfall scenario triggering slope failures. Data observation indicates tha

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