Optimizing Reliability Test Design Using the Critical Failure Mode Prioritization (CFMP) Method

Liquid nitrogen (LN2) based refrigerated transportable standalone system named “SHIVAY” has been developed at RRCAT for transportation and storage of perishable fruits and vegetables (F&V) at required temperature. Reliable and safe operation of refrigerated system is crucial for preserving quality of stored F&V in container during transportation. Design Failure Mode and Effect Analysis (DFMEA) of SHIVAY to diagnose, evaluate and alleviate probable design failures is discussed in this paper. The analysis perceives breakdown of components, identifies probable failure modes, assess the effect on performance/safety and evaluates the risk involved. The DFMEA provides Risk Priority Numbers (RPN) for various possibilities of failure. High RPN number events have been identified like solenoid valve malfunction, leak in heat exchanger etc. and proper measures have been incorporated to improve reliability. DFMEA provided a detailed assessment for the possible design improvements to ensure operational safety and performance of system.

Application of failure mode and effect analysis in an assisted reproduction technology laboratory

Failure Modes and Effects Analysis (FMEA) is an effective technique for identifying and eliminating possible failures. FMEA has been widely used in the manufacturing industry and in various phases of the life cycle of a product. The aim of FMEA is to ensure high quality and reliability. Design FMEA (DFMEA) is used to identify and analyze potential failure modes and their associated causes for product design. It is a verification activity that can help avoid a large percentage of product design problems before the design is finalized. When dealing with new generations of a product including design changes on the basis of existing ones, DFMEA of earlier generations can be used as a reference for such design modifications. Traditionally, criticality or risk assessment in FMEA is performed by developing a risk priority number (RPN). Failure modes that have high RPN values are assumed to be more critical and deserve more attention. In this paper, we propose a method to assess failure modes in DFMEA considering two RPNs for diesel engines. In addition to the traditional RPN, a new RPN is proposed. A diesel engine cylinder head is used as an example to illustrate the application of the two RPNs in a design FMEA.

SUMMARY & CONCLUSIONSTesting of complex products is typically carried out within planned timelines and cycles to assess system-level reliability. However, despite these efforts, overall reliability targets are often not met, and there remains ambiguity around the appropriate point to end testing and how to measure its effectiveness. Ending testing too early risks overlooking critical failure modes, which can result in elevated field failure rates. Key challenges include determining when sufficient testing has been performed and whether all significant failure modes have been identified [1].This paper presents a method for defining the optimal stopping point for testing during the development phase of complex systems. It also proposes a framework for evaluating the effectiveness of system-level testing based on the discovery of failure modes or symptoms throughout various stages of product development.The proposed method shares conceptual similarities with the Crow AMSAA model [2] in terms of data collection, treating each unique failure mode as a contributor to reliability growth. By tracking each distinct failure or symptom, the method provides insights into design maturity, the effectiveness of system testing in exposing failure modes, and the duration required to uncover them. Failure modes and symptoms are categorized into five progressive phases:This categorization helps guide test teams in deciding whether continued testing is justified and whether critical failure modes have been sufficiently uncovered.Analyzing the rate at which new failure modes are discovered allows for more accurate estimation of test duration for current and future programs. Additionally, it offers a quantifiable measure of test effectiveness, supporting the optimization of test strategies and methodologies.

Unexpected failures in process industries lead to production loss and an increase in the cost of maintenance as well. Reliability, Availability, and Maintainability (RAM) analysis is a helpful tool in reducing the number of failures. Also, it helps to increase the mean time between failures by carrying out necessary design modifications with an appropriate improvement in the maintenance schedule. The present study examines the effect of critical failure modes on the reliability of the Effluent Treatment and Injection plant (ETIP) of an oil field. The essential equipment of the plant is identified, which upon failure could result in downtime of the facility. Based on the field data and OREDA database, probable failure rates in each critical mode and mean active repair time are developed with the help of Reliability Block Diagrams (RBD) using the BLOCKSIM™ tool. Using Monte Carlo approach, performance of the system is analyzed in the critical failure modes over the lifetime. It is seen that the reliability of the chosen process plant decreases with time in the critical failure modes while the combined effect of all critical failures on the reliability is about 5%. While criticality of the analysis depends on estimates of failure rates and the repair time of essential equipment, the mean availability of the plant is estimated to be 97.62%.

Critical loads and load-carrying capacities for steel scaffolds used as shoring systems were compared using computational and experimental methods in Part I of this paper. In that paper, a simple 2-D model was established for use in evaluating the structural behavior of scaffold-shoring systems. This 2-D model was derived using an incremental finite element analysis (FEA) of a typical complete scaffold-shoring system. Although the simplified model is only two-dimensional, it predicts the critical loads and failure modes of the complete system. The objective of this paper is to present a closed-form solution to the 2-D model. To simplify the analysis, a simpler model was first established to replace the 2-D model. Then, a closed-form solution for the critical loads and failure modes based on this simplified model were derived using a bifurcation (eigenvalue) approach to the elastic-buckling problem. In this closed-form equation, the critical loads are shown to be function of the number of stories, material properties, and section properties of the scaffolds. The critical loads and failure modes obtained from the analytical (closed-form) solution were compared with the results from the 2-D model. The comparisons show that the critical loads from the analytical solution (simplified model) closely match the results from the more complex model, and that the predicted failure modes are nearly identical.

Portable electronic products subjected to transient shock may exhibit multiple failure modes in vicinity of the interconnects. Failure is often diagnosed by loss of functionality using techniques including the built-in self test, which provides limited insight into reliability and remaining useful life. In this paper prognostic framework for electronic systems has been developed with neural network based self organizing maps. The presented approach resides in the pre-failure space with a focus on electronic systems with multiple failure modes. Unsupervised learning of the neural net has been used to train the neural net for identification of individual failure modes. Feature vectors have been developed based on damage pre-cursors from time-spectral measurements. The clustered damage pre-cursors have been correlated with failure modes of the underlying damage. Several chip-scale packages have been studied, with leadfree second-level interconnects including SAC305, SAC405 alloys. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. Fault modes predicted by simulation based pre-cursors have been correlated with those from experimental data. Activation of different neurons in the lattice for various failure modes and combinations of failure modes has been demonstrated. Previously the authors have developed techniques based on statistical pattern recognition for leading indication of impending failure and detection of damage initiation and progression [Lall 2006 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> , 2007 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> , 2008]. Early classification of multiple failure modes in the pre-failure space is new.

One of the most cost-effective methods of completing an openhole well for oil and gas production is to gravel pack the openhole interval using sand screens and sand management devices to control the production of formation sand. In general, hydrocarbons can be produced from wells in one zone or wells spanning several production zones. As the wells get deeper, producing from multiple zones becomes a cost-effective option, especially in offshore environments. The most-common method to achieve gravel packing for multizone openhole completions is to use a conduit system—either a set of tubes or a concentric sleeve to bypass the isolation device and transport the proppant to the lower zones. Operationally, the tubes are designed to withstand screenout pressure. Screenout is a pressure spike that occurs at the end of gravel packing, indicating the zones has packed. The current systems are designed for low to medium screenout pressures of up to 6,000 psi. But for deepwater wells, the tubes need to be designed to withstand greater screenout pressures to provide a reliable openhole gravel-pack sand control solution for extended-length, multizone, and high-pressure, high-temperature wells. In addition, the leak path through the shunt tubes for fluid migration between producing and nonproducing intervals was a concern to achieve total zonal isolation. This paper discusses a comprehensive design approach for the rapid development of a high-pressure conduit system for total zonal isolation following gravel-packing operations. The architecture goal was to make no changes to the packer seal and setting system to enable concurrent manufacturing of the packer. A comprehensive risk matrix using design failure mode and effects analysis (DFMEA) was established to capture risks and formulate a derisking strategy. The significant risks were evaluated using finite element analysis (FEA) techniques, and extensive modeling and simulation were performed on pressure and buckling failures. A comprehensive computational fluid dynamics (CFD) analysis was performed to simulate the erosion of the system during gravel packing. The system was modeled with reduced thickness, and pressure simulation was performed using FEA at a minimum material condition to verify the design. All failure modes were studied; they were first analyzed and verified in FEA/CFD before proceeding to qualification testing to provide a greater chance of passing the test on the first attempt, thereby reducing the development time and cost spent on test iterations. The new gravel-pack enhanced shunt-tube system was successfully pressure and erosion tested on the first attempt. The full system integration was successfully completed with no delays or redesign required of the enhanced shunt tube or the packer, enabling the rapid development of the high-pressure bypass system. A multizone openhole gravel-pack completion was installed successfully in the field with the enhanced shunt alternate path tube (ESAPT) screen, high-pressure shunted mechanical packers (SMPs), and shunt-tube isolation valves (STIV) to provide an improved operating pressure envelope and erosion tolerance. The high-pressure SMPs help provide an integrated service—sand control pumping, openhole packers, and shunt isolation technology to reduce capital expenditures by reducing rig time and well count, especially in deepwater wells.

The aim of the paper is to investigate the shear failure mechanisms in T-shape, single span and simply supported beams exclusively reinforced with longitudinal glass fiber reinforced polymer (GFRP) bars. Usually the critical shear crack in RC beams without stirrups develops through the theoretical compression strut reducing the shear strength following the shear failure. The main parameter affecting the crack pattern and the shear strength of the beams is the shear slenderness. However, the test results presented in the paper indicated the new arching effect due to the bond losing between the GFRP flexural reinforcement and concrete. This failure mode revealed unexpected critical crack pattern and failure mode. The research of concrete beams flexurally reinforced with GFRP bars without stirrups indicated two failure modes: typical shear-compression and a new one leading by the bond losing between the ordinary reinforcement and concrete.

SUMMARY & CONCLUSIONSFailure Mode and Effects Analysis (FMEA) is essential in reliability engineering but is often manual and time-consuming. This work implements local version of a semi-automated workflow that integrates Microsoft Excel, Python (using the xlwings package), and ChatGPT to accelerate and standardize FMEA generation while retaining engineering oversight.Engineers begin by entering the system context in an Excel worksheet; a Python back end then queries ChatGPT to generate an initial list of system components and candidate failure modes. Engineers review and refine these suggestions, define system boundaries and each component’s function, and approve or modify the items captured in designated Excel cells. The Python functions prompt ChatGPT with component context and repair/warranty data so that suggested failure modes, effects, and causes are grounded in organization-specific evidence rather than generic model knowledge.The implemented workflow produces FMEA entries that align more closely with observed field failure trends by incorporating repair and warranty data into prompt context. Auto-population of failure modes, causes, and effects substantially reduces routine authoring effort; initial tests indicate markedly faster completion times and broader coverage of plausible failures. Offloading routine content generation to the LLM allows engineers to focus on analysis, validation, and prioritization, improving consistency and the effective use of engineering judgment.Combining a familiar spreadsheet interface with Python automation and ChatGPT produces a practical, deployable semi-automated FMEA process. The approach enables reliability teams to generate richer, data-informed FMEA results more quickly, enhancing productivity and providing deeper insight into failure analysis. Planned Version 2 (cloud) work will include user-upload able, product- and system-specific knowledge bases (with macros to upload and tag documents to the cloud) so that subsequent Design Failure Mode and Effects Analysis (DFMEA) for the same system can limit retrieval-augmented generation (RAG) retrieval to a curated, relevant document set.

Purpose: This paper proposes methods and procedures to effectively identify and select potential failure modes in the performance of faliure mode and effect analysis (FMEA). Specifically, a method is proposed to perform FMEA with emphasis on deriving failure mechanisms.<BR>Methods: The failure mechanism is known for a design change or improvement preventing recurrence and proactive. Therefore, it is important in designing FMEA to derive failure mechanisms and potential failure modes. This paper presents methods and procedures to derive failure mode and failure mechanism by analyzing past failure cases, usage/environmental condition parts (material), and interaction.<BR>Results: The effective design FMEA implementation procedure in the inorganic system development phase was described through the design FMEA practice case for antenna circuit card assembly and HL motor assembly among the drives used in induction weapon systems.<BR>Conclusion: The effectiveness of new identification of failure modes can be identified that the existing FMEA had not predicted by applying the proposed effective FMEA implementation methods and procedures to the weapon system development model.

Utilisation de l’insuline en établissement de santé universitaire mère–enfant : analyse des modes de défaillance

A hybrid quality control (QC) program was developed that integrates automated and conventional Linac QC, realizing the benefits of both automated and conventional QC, increasing efficiency and maintaining independent measurement methods. Failure mode and effects analysis (FMEA) was then applied in order to validate the program prior to clinical implementation. The hybrid QC program consists of automated QC with machine performance check and DailyQA3 array on the TrueBeam Linac, and Delta4 volumetric modulated arc therapy (VMAT) standard plan measurements, alongside conventional monthly QC at a reduced frequency. The FMEA followed the method outlined in TG‐100. Process maps were created for each treatment type at our center: VMAT, stereotactic body radiotherapy (SBRT), conformal, and palliative. Possible failure modes were established by evaluating each stage in the process map. The FMEA followed semiquantitative methods, using data from our QC records from eight Linacs over 3 years for the occurrence estimates, and simulation of failure modes in the treatment planning system, with scoring surveys for severity and detectability. The risk priority number (RPN) was calculated from the product of the occurrence, severity, and detectability scores and then normalized to the maximum and ranked to determine the most critical failure modes. The highest normalized RPN values (100, 90) were found to be for MLC position dynamic for both VMAT and SBRT treatments. The next highest score was 35 for beam position for SBRT, and the majority of scores were less than 20. Overall, these RPN scores for the hybrid Linac QC program indicated that it would be acceptable, but the high RPN score associated with the dynamic MLC failure mode indicates that it would be valuable to perform more rigorous testing of the MLC. The FMEA proved to be a useful tool in validating hybrid QC.

PurposeThe purpose of this paper is to identify critical equipment by dynamically ranking them in interval-valued intuitionistic fuzzy (IVIF) circumstances. Accordingly, the main drawbacks of the conventional failure mode and effects analysis (FMEA) are eliminated. To this end, the authors have presented the interval-valued intuitionistic fuzzy condition-based dynamic weighing method (IVIF-CBDW).Design/methodology/approachTo realize the objective, the authors used the IVIF power weight Heronian aggregation operator to integrate the data extracted from the experts’ opinions. Moreover, the multi-attributive border approximation area comparison (MABAC) method is applied to rank the choices and the IVIF-CBDW method to create dynamic weights appropriate to the conditions of each equipment/failure mode. The authors proposed a robust FMEA model where the main drawbacks of the conventional risk prioritization number were eliminated.FindingsTo prove its applicability, this model was used in a case study to rank the equipment of a HL5000 crane barge. Finally, the results are compared with the traditional FMEA methods. It is indicated that the proposed model is much more flexible and provides more rational results.Originality/valueIn this paper, the authors have improved and used the IVIF power weight Heronian aggregation operator to integrate information. Furthermore, to dynamically weigh each equipment (failure mode), they presented the IVIF-CBDW method to determine the weight of each equipment (failure mode) based on its equipment conditions in the O, S and D criteria and provide the basis for the calculation. IVIF-CBDW method is presented in this study for the first time. Moreover, the MABAC method has been performed, to rank the equipment and failure mode. To analyze the information, the authors encoded the model presented in the robust MATLAB software and used it in a real sample of the HL5000 crane barge. Finally, to evaluate the reliability of the model presented in the risk ranking and its rationality, this model was compared with the conventional FMEA, fuzzy TOPSIS method, the method of Liu and the modified method of Liu.

The towing system is one of the most crucial parts of a tug boat. The towing process in tugboats, where a lot of machines and ship equipment are involved in the process, carries many risks and potential failures. Therefore, this operation can cause serious injuries, fatal accidents, equipment damage, disruption of towing operations and loss of time and money. In this study, a comprehensive risk analysis was conducted to increase the reliability of the tugboat towing system. Firstly, the failure modes, causes and results of the towing system were determined. Then, the failure modes and effect analysis method were integrated with the rule-based fuzzy approach and risk prioritization was performed. According to the findings, the three critical failure modes among 23 failure modes were towing system rope failure, insufficient air supply to the towing system from the compressor and cracking or breaking of system components, respectively. Additionally, corrective and preventive actions were suggested for a total of eleven failure modes with high RPN values. This study aims not only to provide a theoretical contribution to the prevention of towing system-related accidents, but also to provide a practical framework that can be used in practice for all maritime stakeholders, especially tugboat owners and crews.