Identify Failure Modes Research Articles

Gear-fault monitoring and digital twin demonstration of aircraft engine based on piezoelectric vibration sensor for engine health management

Aircraft engine vibration signals carry crucial operational information, necessitating accurate monitoring and analysis to ensure flight safety. However, the detection accuracy of vibration sensors is significantly compromised in high-temperature environments, posing substantial challenges for traditional battery-powered models. Here, we present a self-powered piezoelectric vibration sensor based on a polyvinylidene fluoride-polyimide nanofiber membrane, which can effectively mitigate the performance degradation in high-temperature environments. The piezoelectric vibration sensor is capable of distinguishing the vibration signals generated by healthy gears, pitting gears, and broken gears, achieving a recognition accuracy of 96.3 % by using machine-learning algorithms. Finally, we develop a digital twin system to identify failure modes in aircraft engine gears, promising intelligent engine health management.

A Process Evaluation Approach to Central Line-Associated Bloodstream Infection Reduction in a Neonatal Population.

To reduce the rate of central line-associated bloodstream infections (CLABSI) in the M Health Fairview Neonatal Intensive Care Unit (NICU) from 2.15 infections per 1,000 central line days to less than one per 1,000 line days using process evaluation. An interdisciplinary team used process mapping and Failure Modes and Effects Analysis (FMEA) to identify root causes and improvement opportunities in central line maintenance. The focus was on neonates born at <32 weeks of gestational age and weighing less than 750 g. Interventions included standardized clean space, algorithms to reduce line access, and standardized kits for line maintenance. Over 4years, the project achieved an 86.6% reduction in CLABSI events, decreasing from 15 events in 2019 to two in 2023. The CLABSI rate dropped by 85%, from 2.15 to 0.32 per 1,000 line days. Statistical analysis indicated a significant reduction in FMEA risk scores for the identified failure modes, with an average reduction of 33%. The process-focused approach and interdisciplinary collaboration significantly reduced CLABSI rates in the NICU. Future efforts should aim to maintain these improvements and achieve a target of zero CLABSI events.

Evaluation of physical risk factors by fuzzy failure mode and effects analysis: an apparel mill example

This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy logic approach. The study found that risk assessment in the apparel industry is more accurate and reliable when using a fuzzy logic approach. Measurements were taken in three different time periods for noise, vibration, thermal comfort and lighting, which are physical risk factors. To evaluate the identified failure modes, the Failure Mode and Effects Analysis (FMEA) method was first applied. Then, for the same failure modes, the fuzzy FMEA method was applied using Fuzzy Logic Designer, an add-on to MATLAB. The study concludes that the fuzzy FMEA method can be a more useful method for risk assessment in the apparel industry.

A hybrid failure analysis model design for marine engineering systems: A case study on alternative propulsion system

Marine engineering systems have a fundamental role in ensuring the efficiency, safety, and sustainability of maritime transportation. The performance and reliability of these systems, particularly propulsion systems, are of paramount importance. This paper introduces a pioneering hybrid failure analysis model that integrate the Variable Weighted Synthesis Analytic Hierarchy Process (VWS-AHP) with the conventional Failure Mode and Effect Analysis (FMEA) methodology. The primary objective is to enhance the comprehension of failure modes within marine engineering systems, particularly focusing on alternative propulsion systems. While conventional FMEA is a widely employed methodology for analysis of failure modes, its capacity and effectiveness can be further augmented by integrating complementary techniques. The proposed hybrid model combines the robustness of FMEA in identifying failure modes with the VWS-AHP method’s ability to handle complex decision-making scenarios involving multiple criteria and varying levels of importance. This integration affords a comprehensive framework to assess failure risks, prioritize critical failure pathways, and evaluate the potential impacts of failures in marine engineering systems. To demonstrate the applicability of the proposed model, we conduct a case study on an alternative propulsion system. The outcomes of the case study showcase the efficacy of the hybrid model in enhancing FMEA.

Risk Assessment of ZAP-X→ Radiosurgery System in a Quaternary Care Hospital Using FMEA Approach

Background and Aims: The overarching goal of this study is to conduct a robust risk assessment using the Failure Modes and Effects Analysis (FMEA) approach. By identifying potential failure modes and their potential impacts on patient care and hospital operations, this research seeks to provide actionable insights and mitigation strategies for the safe and effective implementation of the ZAP-X Radiosurgery System. The swift integration of advanced medical technologies, exemplified by the ZAP-X→ Gyroscopic Stereotactic Radiosurgery System, a computer-controlled machine that delivers radiation to an intended target in the brain, necessitates a comprehensive proactive risk assessment before the launch of the system to ensure the safety of patients, staff and operational processes. Method: Applying the FMEA methodology, we meticulously mapped all processes and sub-processes associated with the ZAP-X Radiosurgery System implementation. Our multidisciplinary team, through a discussion on ‘what can go wrong’, identified failure modes. Risks were prioritised based on severity, likelihood and detectability giving them a Risk Priority Number (RPN). The team proposed mitigation strategies, deploying controls to enhance safety and reliability.

Mitigating Risks in Cone Beam Computed Tomography Guided Online Adaptive Radiation Therapy: A Preventative Reference Planning Review Approach

PurposeOnline adaptive radiotherapy (oART) treatment planning requires evaluating the temporal robustness of reference plans, anticipating the potential changes during treatment courses that may even lead to risks unique to the adaptive workflow. This study conducted a risk analysis of the CBCT-guided adaptive workflow and is the first to assess an adaptive specific reference planning review that mitigates risk in the planning process to prevent events and treatment deficiencies during adaptation. Methods and MaterialsA quality management team of medical physicists, residents, physicians, and radiation therapists performed a fault tree analysis, and failure mode and effects analysis (FMEA). Fault trees were created for under/overdosing targets, treatment deficiencies, as well as assisted in identifying failure modes for the FMEA. Treatment deficiency was defined to include a non-ideal oART plan resulting in treatment with a lower quality plan (either oART or scheduled plan), treatment delay, or cancelling treatment for the day. A reference planning checklist was created to catch failure modes before reaching the patient. Risk priority numbers (RPN=Severity*Detectability*Occurrence) were scored with and without the reference planning checklist to quantify risk mitigation. A root cause analysis was conducted for an event where an adaptive plan failed to generate. ResultsThe reference planning checklist (with items covering patient background, contouring/planning robustness for anatomy variability, and machine limitations) reduced the RPN for all failure modes. Only 1 failure mode with a risk priority number >150 occurred with the reference planning checklist compared to 29 failure modes without, including 14 adaptive specific failure modes. Contouring, planning, setup, scheduling, and documentation errors were identified during the fault tree analysis. 29/70 errors were adaptive specific. The reference planning checklist could address 23/33 for over/under dosing and 28/37 errors for treatment deficiency. The root cause analysis highlighted the need for checking setup prior to adaptive plan delivery, and time-out checklist. ConclusionsThe reference planning checklist improved detection of the failure modes and improves the quality and robustness of the plans produced for oART. It is ideally performed before physician plan review to prevent last minute re-plan (before or after first adaptive treatment) and delay of patient start. The checklist presented can be modified based on failures specific to individual clinics and utilized at various planning steps based on available resources.

Improving the safety of radiotherapy treatment processes via incident-driven FMEA feedback loops.

Failure mode and effects analysis (FMEA) is a valuable tool for radiotherapy risk assessment, yet its outputs might be unreliable due to failures not being identified or due to a lack of accurate error rates. A novel incident reporting system (IRS) linked to an FMEA database was tested and evaluated. The study investigated whether the system was suitable for validating a previously performed analysis and whether it could provide accurate error rates to support the expert occurrence ratings of previously identified failure modes. Twenty-three pre-identified failure modes of our external beam radiotherapy process, covering the process steps from patient admission to treatment delivery, were proffered on dedicated FMEA feedback and incident reporting terminals generated by the IRS. The clinical setting involved a computed tomography scanner, dosimetry, and five linacs. Incoming reports were used as basis to identify additional failure modes or confirm initial ones. The Kruskal-Wallis H test was applied to compare the risk priorities of the retrospective and prospective failure modes. Wald's sequential probability ratio test was used to investigate the correctness of the experts' occurrence ratings by means of the number of incoming reports. Over a 15-month period, 304 reports were submitted. There were 0.005 (confidence interval [CI], 0.0014-0.0082) reported incidents per imaging study and 0.0006 (CI, 0.0003-0.0009) reported incidents per treatment fraction. Sixteen additional failure modes could be identified, and their risk priorities did not differ from those of the initial failure modes (p=0.954). One failure mode occurrence rating could be increased, whereas the other 22 occurrence ratings could not be disproved. Our approach is suitable for validating FMEAs and deducing additional failure modes on a continual basis. Accurate error rates can only be provided if a sufficient number of reports is available.

Reducing overnight charges in the loading process of finished goods using FMEA method at PT. XYZ

As a part of the supply chain, the logistics department plays a crucial role in the operational activities of a company. Like other departments, the logistics department also incurs costs that do not add value, such as overnight charges. Failure in the loading of finished goods process can result in a dwell time exceeding 18 hours per vehicle, leading to overnight charges. This occurrence can be prevented by identifying risks and failure modes using Failure Mode and Effect Analysis (FMEA). The FMEA methodology is widely utilized across various industries, including manufacturing, for product development. Additionally, FMEA is frequently employed in failure analysis in service industries, food services, construction, mining, agriculture, and healthcare. The research was conducted to identify failure modes and potential causes in the loading of finished goods process. In PT. XYZ, there are five finished goods storage areas. From the process of weigh-in time, loading finish goods, and weigh-out time, 25 activities were analyzed, and seven activities with the highest Risk Priority Number (RPN) were selected for solutions. The activity with the highest RPN, scoring 512, was the queuing of trucks exceeding loading capacity. The why-why analysis technique was employed to assist in finding solutions. From the seven activities with the highest RPN, 9 solution plans were derived. The recommendation to reduce queue trucks is to organize and monitor the vehicles dispatched by the Freight Forwarder. As a result, the dwell time per vehicle decreased from 9.23 hours to 6.36 hours, and the overnight charges reduced from 148,145 USD to 12,345 USD

Sliding Window Optimal Transport for Open World Artifact Detection in Histopathology.

Histological images are frequently impaired by local artifacts from scanner malfunctions or iatrogenic processes - caused by preparation - impacting the performance of Deep Learning models. Models often struggle with the slightest out-of-distribution shifts, resulting in compromised performance. Detecting artifacts and failure modes of the models is crucial to ensure open-world applicability to whole slide images for tasks like segmentation or diagnosis. We introduce a novel technique for out-of-distribution detection within whole slide images, compatible with any segmentation or classification model. Our approach tiles multi-layer features into sliding window patches and leverages optimal transport to align them with recognized in-distribution samples. We average the optimal transport costs over tiles and layers to detect out-of-distribution samples. Notably, our method excels in identifying failure modes that would harm downstream performance, surpassing contemporary out-of-distribution detection techniques. We evaluate our method for both natural and synthetic artifacts, considering distribution shifts of various sizes and types. The results confirm that our technique outperforms alternative methods for artifact detection. We assess our method components and the ability to negate the impact of artifacts on the downstream tasks. Finally, we demonstrate that our method can mitigate the risk of performance drops in downstream tasks, enhancing reliability by up to 77%. In testing 7 annotated whole slide images with natural artifacts, our method boosted the Dice score by 68%, highlighting its real open-world utility.

Production risk management of prawn crackers using fuzzy FMEA and fuzzy AHP methods

Prawn crackers hold a significant position within Indonesia's agro-industrial product landscape. The industrial process involved in their production is fraught with potential risks that could result in substantial losses for companies. PT. XYZ, a prominent prawn cracker producer in Indonesia, grapples with various risks, including non-compliant raw material supplies, machinery and equipment breakdowns, product defects, and process failures. Hence, there is an imperative need for comprehensive risk management involving further analysis, identification of potential failure modes, and formulation of mitigation strategies to curb losses. This research aims to ascertain the priority of potential failure modes and their corresponding mitigation strategies in the prawn cracker production process at PT. XYZ. The study employs risk analysis through the Fuzzy Failure Mode and Effect Analysis (Fuzzy-FMEA) method and determines mitigation strategies using the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) method. The findings reveal 11 identified failure modes in PT. XYZ's prawn cracker production process, with machinery and equipment damage emerging as the top-priority failure mode, giving a Fuzzy Risk Priority Number (FRPN) value of 7.620. The recommended mitigation strategy involves intensifying inspections of production machinery and equipment, both before and after use by workers, with a weight of 0.368.

Analysis of risk priority number of FMEA and surprise index for components of 7 kW electric vehicle charger

The purpose of this study is to suggest systematic management measures by analyzing the risk of related part through FMEA of RPN index based on severity, occurrence, detection of 543,900 7 kW EV chargers installed until 2023 in South Korea and SI based on severity, detection and information score scale. To achieve this goal, this research investigated the current status of 7 kW EV chargers, and categorized the parts and assemblies of the investigated chargers. The failure mode of classified components was explored based on the 2016 edition of FMD, and the failure mode rate and information score of SI were referred to data contained in Nprd 2016, Eprd 2014; Fmd 2016. The identified failure mode, failure causes, and failure effects were described by domestic experts in EV charger-related sectors.There were 4 risk factors (10%) felt under “Group A″, the upper 10% of RPN. Of these, inverter overheating ranked in the highest. Moreover, the analysis revealed one case of corrosion of the enclosure and overheating in the main board and communication board each. The effect of overheating was confirmed in these components excluding the enclosure. From the SI perspective, high values were obtained in 4 parts of AC/DC breaker, one of connector, and one of cable. Based on the extracted data, management strategies were suggested.

Pengendalian Kualitas Produk Gula Studi Kasus PT Madubaru PG. PS. Madukismo Yogyakarta

PG Madukismo is a sugar factory that was established in 1955 and is the only sugar factory in the province of the Special Region of Yogyakarta. This factory is located in Padokan Village, Tirtonirmolo Village, Kasihan District, Bantul Regency, Special Region of Yogyakarta. PT Madubaru, a company engaged in sugar cane processing, is a sugar and spirits factory located in the Yogyakarta area. Companies that produce cane sugar with SHS IA quality. Product defects are often found, resulting in a decrease in quality, therefore it is necessary to carry out quality control. This study uses the Failure Mode and Effect Analysis (FMEA) method to identify failure modes, Logic Tree Analysis (LTA) to determine the consequences of failure arising from failure modes and determine effective preventive maintenance policies for each machine. For the production quality control section, the Statistical Processing Control (SPC) method is used to analyze product defects to determine whether product defects are still within controllable limits or not. While the Failure Mode and Effect Analysis (FMEA) method is used to determine the failure mode in sugar production. For the quality control section on raw materials using the Economic Order Quantity (EOQ), Re-Order Point (ROP), Safety Stock (SS), and Total Inventory Cost (TIC) methods. The conclusion of this study is regarding the results of the analysis and calculations carried out with several settlement methods and there are several causes of production defects from 5 factors, such as: human factors, work methods, environment, raw materials and machines used. And provide solutions to companies in order to reduce defects by controlling product quality.

Machine-Learning and Physics-Based Tool for Anomaly Identification in Propulsion Systems

Launch anomalies occur frequently during the early phase of a program, with many of the anomalies attributed to propulsion systems. Approaches for identifying and mitigating potential propulsion failures can aid development programs and accelerate the resolution of root cause investigations. In reusable systems, anomaly detection methods can be employed to detect latent system health issues that could become problematic as the system ages. Modern launch support relies on human judgement for redline limit generation and visual family data comparison for many operational aspects, which makes it challenging to identify failure modes and to diagnose an anomaly. Additionally, family data comparison is unavailable for the first few launches of a new vehicle. Automated tools to quickly identify system failures of new and reusable systems can bridge these gaps. Physics-based modeling and machine learning (PBMML) offers methods that can improve the reliability of new or reusable launch vehicles by identifying propulsion anomalies or issues before they jeopardize future space missions. PBMML can then be used to inform corrective actions. This paper describes an anomaly data generation module which automates the process of simulating anomalous scenarios in launch vehicle and fluid networks, while a long-term short-term memory network is used to provide real-time anomaly classification on a simplified stage test case.

Use of a Modified Threshold Function in Fuzzy Cognitive Maps for Improved Failure Mode Identification

Fuzzy cognitive maps (FCMs) provide a rapid and efficient approach for system modeling and simulation. The literature demonstrates numerous successful applications of FCMs in identifying failure modes. The standard process of failure mode identification using FCMs involves monitoring crucial concept/node values for excesses. Threshold functions are used to limit the value of nodes within a pre-specified range, which is usually [0, 1] or [-1, + 1]. However, traditional FCMs using the tanh threshold function possess two crucial drawbacks for this particular.Purpose(i) a tendency to reduce the values of state vector components, and (ii) the potential inability to reach a limit state with clearly identifiable failure states. The reason for this is the inherent mathematical nature of the tanh function in being asymptotic to the horizontal line demarcating the edge of the specified range. To overcome these limitations, this paper introduces a novel modified tanh threshold function that effectively addresses both issues.

Hybrid Machine Learning Algorithms for Prediction of Failure Modes and Punching Resistance in Slab-Column Connections with Shear Reinforcement

This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model’s predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction.

Evaluation of the risk of occupational exposure to antineoplastic drugs in healthcare sector: part II - the application of the FMECA method to compare manual vs automated preparation.

Healthcare workers handling antineoplastic drugs (ADs) in preparation units run the risk of occupational exposure to contaminated surfaces and associated mutagenic, teratogenic, and oncogenic effects of those drugs. To minimise this risk, automated compounding systems, mainly robots, have been replacing manual preparation of intravenous drugs for the last 20 years now, and their number is on the rise. To evaluate contamination risk and the quality of the working environment for healthcare workers preparing ADs, we applied the Failure Mode Effects and Criticality Analysis (FMECA) method to compare the acceptable risk level (ARL), based on the risk priority number (RPN) calculated from five identified failure modes, with the measured risk level (MRL). The model has shown higher risk of exposure with powdered ADs and containers not protected by external plastic shrink film, but we found no clear difference in contamination risk between manual and automated preparation. This approach could be useful to assess and prevent the risk of occupational exposure for healthcare workers coming from residual cytotoxic contamination both for current handling procedures and the newly designed ones. At the same time, contamination monitoring data can be used to keep track of the quality of working conditions by comparing the observed risk profiles with the proposed ARL. Our study has shown that automated preparation may have an upper hand in terms of safety but still leaves room for improvement, at least in our four hospitals.

Towards a Regulatory Framework for Electronic Medical Record Interoperability in Canada.

All complex systems are potentially predisposed to failure. Healthcare systems are complex systems that are prone to many errors that can result in dire consequences for patients and healthcare providers. The healthcare system in Canada is under unprecedented strain due to shortages of healthcare providers, provider burnout, inefficient workflows, and a lack of appropriate digital infrastructure. We used failure mode and effects analysis (FMEA) to identify the failure modes for care provided in primary care settings. We identified failure modes in appointment scheduling, patient-provider communications, referrals, laboratory and diagnostic procedures, and medication prescriptions as the main failure modes. To mitigate the detected risks, we recommend solutions to 'close the loop' on failure modes to prevent patients from falling through the cracks, as vulnerable patients who cannot advocate for themselves are most likely to do so. We provide preliminary requirements for a regulatory regime for electronic health records that can reduce provider burnout, improve regulatory compliance, and improve system efficiency, all while improving patient safety, experience, and outcomes.

Application of FMEA and FTA techniques for assessing the occurrence of LOCA in a TRIGA MARK I Reactor

This study presents a framework merging failure mode and effect analysis (FMEA) and fault tree analysis (FTA) techniques to evaluate loss of coolant accident (LOCA) in the Brazilian Reactor TRIGA IPR-RI. At first, FMEA identified failure modes and prioritized risk management based on the risk priority number (RPN). Subsequently, FTA established logical relationships between these failure modes, revealing minimal cut sets. Results indicated that the path involving “loss of coolant from reactor tank” correlate to an externally caused leak, exhibiting a low probability and, consequently, a low RPN. On the other hand, the path involving “loss of coolant through primary circuit rupture without a pump shutdown” highlighted high RPN due to the lack of shutdown signals from the monitoring indicators. Finally, FTA unveiled that LOCA associates with a primary circuit rupture and the simultaneous failure of safety mechanisms, including ultimately human error.

FMEA-based risk management improves the ability of oral healthcare personnel to prevent needlestick injuries.

To explore the application value of the Failure Mode and Effects Analysis (FMEA) method in the risk management of needlestick injuries among oral healthcare personnel. A total of 37 healthcare workers from the dental department of Zhujiang Hospital, Southern Medical University, were selected as study subjects. Routine risk management procedures were followed from January 2021 to December 2021, serving as the control group, while FMEA-based risk management was implemented from January 2022 to December 2022, representing the research group. The Risk Priority Number (RPN) was calculated, and interventions were implemented for the top five identified failure modes. The RPN score, incidence of needlestick injuries, healthcare personnel's knowledge and awareness levels, prevention behavior, and rate of satisfaction with management were compared between the two groups. FMEA-based risk management identified weak knowledge of protection, disorganized placement of sharp instruments, failure to adhere to operational standards, improper operational procedures, and insufficient regulations for preventing needlestick injuries as the top five failure modes. The RPN scores for these modes were significantly lower in the research group (P<0.05). The research group also experienced a lower frequency and incidence of needlestick injury (P<0.05), along with higher levels of healthcare knowledge, awareness of prevention, and prevention behavior (P<0.05). Additionally, satisfaction with management was higher in the research group compared to the control group (P<0.05). FMEA-based risk management can improve the ability of oral healthcare personnel to prevent needlestick injury, reduce the occurrence of such incidents, and enhance satisfaction with management. This approach holds promise for wider adoption.

An ensemble model of explainable soft computing for failure mode identification in reinforced concrete shear walls

Shear walls are critical structural components that resist lateral forces. The failure mode of a shear wall directly impacts the overall structural behavior. Therefore, accurate identification of the failure mode of shear walls is essential for the design, construction, and maintenance of safe and reliable structures. This paper presents an ensemble learning (EML) approach that integrates various different types of models to predict the failure mode of shear walls. Utilizing 393 shear wall samples gathered from the literature, a new EML model with better generalization performance was constructed using random forest (RF), support vector machine (SVM), light gradient boosting machine (LGBM), and logistic regression (LR) models, and these sub-models were further optimized using bayesian optimization (BO). The results demonstrate that the EML model achieved the highest recognition accuracy at 92.37 % in eight models, and the prediction performance of sub-models significantly improved after optimization. Multiple model interpretation methods revealed that variables such as aspect ratio, wall length-to-thickness ratio, cross-sectional shape, and boundary element reinforcement indices play a particularly influential role in predicting failure mode. This study provides an in-depth explanation of the identified failure modes from both an overall and sample-specific perspective and constructs a graphical user interface (GUI) for demonstrating the recognition of failure modes. Overall, as a study of ensemble learning using different types of models to recognize the failure mode of shear walls, the EML model established in this paper can effectively predict the failure mode of reinforced concrete shear walls.