Robotic System Reliability Analysis and RUL Estimation Using an Iterative Approach

Criticality analysis is a technique for the assessment of criticality rating for every constitutive part. Failure mode effect and criticality analysis (FMECA) are broadly utilized for characterizing, distinguishing and dispensing with potential failures from system, design, or process for the criticality analysis. The determination of the critical ranking of failure modes for criticality analysis is a vital issue of FMECA. The traditional method of FMECA determines the critical ranking of failure modes using the risk priority numbers, which is the product of evaluation criteria like the occurrence, severity and detection of each failure mode but it may not be realistic in some applications. The practical applications reveal that the criticality analysis using traditional FMECA has been considerably criticized for several reasons. In this paper, first, a detailed FMEA to find out the various failure modes of each component of a conventional lathe machine is performed and thereafter, the Fuzzy FMECA approach is used to perform the criticality analysis. A comparative analysis of fuzzy FMECA with traditional FMECA is also done to find out the most superior approach for the criticality analysis. It was concluded that the fuzzy FMECA approach is the most superior approach for the criticality analysis of a system.

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.

The objective of this contribution is to provide a review and suggest possible extensions of the Failure Mode Effects Analysis (FMEA), Hazard Risk Assessment (HRA) [2] and to demonstrate the importance of these tools to general probabilistic design for reliability (PDfR) [8]. FMEA was first introduced in the 1960s by the U.S. National Aeronautics and Space Administration (NASA) and is currently used extensively across many industries. FMEA is useful in understanding the failure modes of various products, qualifying the effects of failure and aiding in the development of mitigation strategies. It is a useful tool in improving quality, reliability, and the maintainability of designs, and is a critical component in risk management strategies and evaluations. This is, actually, the approach of the prognostics and health monitoring/management (PHM) engineering. Failure mode effects and criticality analysis (FMECA) [1] is an extension of (FMEA). While FMEA is a bottom-up, inductive analytical method which may be performed at either the functional or piece-part level, FMECA extends FMEA by including a criticality analysis that is aimed, like PDfR is, at charting the probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value. FMECA tends to be preferred over FMEA in space and North Atlantic Treaty Organization (NATO) military applications, while various forms of FMEA predominate in other industries. Being extensions of the FMEAs, FMECAs add severity and probability ranking aspects to the problems of interest. This is accomplished through an appropriate HRA - an engineering process of where the risk of an event is quantified by examining the chain of the preceding events, starting with, e.g., the failure mode, then stepping through to the end effects. The approach allows quantification of risk through the use of probabilistic risk analysis (PRA) and is addressed and discussed in detail. Failure oriented accelerated testing (FOAT) [9] could and should be viewed as an important constituent part of the effort. It is shown that care must be taken to establish the appropriate probabilities, to identify the statistical independence of the random variables of importance, as well as to assess the trustworthiness of the available or obtained data. It is indicated that an important drawback of the FMEA is the lack of pure operational (field) failure data. These data are frequently utilized from the computerized maintenance management system (CMMS) software, which does not always provide a true snapshot of the Mean Time Between Failures (MTBF) or other critical characteristics of the product. This results in the situation that personal judgment plays a large part in the development of the FMEA. Several papers have been published recently on development of Fuzzy FMEA methodologies (see, e.g., [7]). This application of fuzzy logic to Hazard Risk Analysis will allow additional uncertainty and inaccuracy to be modeled throughout FMECA development, leading to a more robust decision making with consideration of various uncertainties.

Using Failure Mode Effects and Criticality Analysis for High-Risk Processes at Three Community Hospitals

Failure mode, effects, and criticality analysis (FMECA) is a proactive quality tool that allows the identification and prevention of the potential failure modes of a process or product. In a conventional FMECA, for each failure mode, three risk parameters, namely frequency, non-detection, and severity are evaluated and a risk priority number (RPN) is calculated by multiplying these parameters to assess one signal criticality. However, in many cases, it suffers from some shortcomings regarding the decision-making and the situation where the information provided is ambiguous or uncertain. This paper describes a new fuzzy multi-criticality approach for improving the use of FMECA by treating FMECA as a fuzzy multi-criteria optimization model. The new approach bases on replacing the calculation of a single criticality with a fuzzy inference system for improving the criticality evaluations which offers five partial criticalities that efficiently and separately calculate the impact of a failure on the environment, personnel, production, equipment, and management. In addition, an analytical hierarchy method (AHP) is used to calculate the priorities weights for each partial criticality and construct a criticality matrix in order to improve the relevance of decision-making. Furthermore, a real case of LPG storage system for ZCINA Hassi Messaoud in Algeria is provided to illustrate the practical implementation of the suggested approach and extremely shows the pertinence of the suggested fuzzy model as decision-making tools in preventing industrial risks with providing encouraging results regarding the criticality estimation and improve decision-making by prioritizing “preventive –corrective actions” and determine the efficient action for each partial criticality to control the risk effectively.

An alternative risk assessment method, known as failure mode effects and criticality analysis (FMECA), is demonstrated on the regional water supply systems (RWSS) in Tucson, AZ, USA that combines delivery of potable and reclaimed water and conveyance of wastewater to a developing area within the Tucson RWSS. The goal of FMECA is to examine the volumetric severity of a component failure on the overall system function by modeling the system under alternative failure modes. Within FMECA, the Risk Priority Number (RPN) is applied to compare the risk criticality between components' failures. To complete FMECA, the Tucson RWSS is represented in a network flow model that optimally allocates flows between sources and demand points to minimize operational costs. Potential failure mode consequences are evaluated from the flow model as the volume of water not delivered to users if the component is unavailable. The volumetric severity of the failure event is converted to an ordinal value using stakeholder judgment. Likelihood of each failure mode is similarly defined by stakeholders on a 1–10 scale. The RPN is then computed as the product of the severity and likelihood. RPN values for all failure modes are then ranked to assess the most critical elements. Alternative system configurations are examined to assess the value of redundancies on the Tucson RWSS resilience.

The failure mode, effect and criticality analysis (FMECA) is a famous bottom-up analytical method for quantifying and ranking critical failures of a product normally at its design stage. At present, as the importance of equipment maintenance increases in industry, FMECA is often applied in maintenance management. In practical cases, equipment is composed of different components, and its maintenance optimization should consider requirements of reliability, availability and costs together. This paper proposes the method called the extended failure mode, effect and criticality analysis (e-FMECA) for equipment maintenance management optimization on the basis of the FMECA theory. This method gives the procedure of analyzing the risks of different failure modes associated with reliability, availability and maintenance costs of equipment. In order to measure these risks of reliability, availability and maintenance costs quantitatively, a fuzzy risk priority method for e-FMECA is developed. Finally, the case study of the e-FMECA method in terms of metro rolling stocks is given. The proposed e-FMECA might be beneficial for selecting the maintenance strategies, maintenance polices and maintenance optimizing objects for maintenance management optimization.

An LNG Plant or any Petrochemical Plant was constructed on the basis of discovered feed gas with full scale capacity. As the feed gas has decreased, the plant capacity is required to be put in idle or mothball mode. In that period, the plant facilities have to be preserved and inspected to maintain the integrity. The static and rotating equipment, electrical equipment, and instrumentations need to be maintained. The focus in this paper is how to preserve the rotating equipment. Since they always have clearances between rotating part and stationary part, nitrogen containment only is not always adequate to preserve the internal parts. Failure Mode Effect and Criticality Analysis (FMECA) is one of the tools to define how to preserve rotating equipment. The analysis considers the following essential factors: duration of idle period, equipment functions, equipment mode of failures, preservation method, test convenience, cost of consequence, and safety. This paper takes the case of FMECA of one of the LNG Process Train in Badak LNG that has undergone idle period. Rotating equipment consists of Steam Turbines, Centrifugal Gas Compressors, LNG Pumps, Hydrocarbon Pumps, High Speed Pumps, Lube Oil Pumps, and Fin Fans. The evaluation is down to its auxiliary parts such as Mechanical Seal, Bearing, and Hydraulic Governors. The FMECA study also defines criticality of equipment in the frame of future as the idle period has ended. Rotating equipment may have issues on corrosion and performance. One possibility is whether the equipment or its internal parts (such as rotors, diaphragms, etc) need to be removed from site to storage or warehouse. The criticality also considers the mode of operation of the equipment and their fluid services. This case study presents evaluation of following fluid services: High Pressure Steam, Medium Pressure Steam, Low Pressure Steam, Lubricating Oil, Amine, Natural Gas, Liquefied Petroleum Gas (LPG), and Liquefied Natural Gas (LNG). As the result of FMECA study, the facility has to be preserved in specific methods and measures based on the period of idle. The method provides recommendation of preservation treatment, storage specification, testing requirement, product lifecycle study. The methods and measures performed during idle period are expected to maintain the facility integrity when it needs to be in operation again.

Failure mode effects and criticality analysis (FMECA) is one of most widely used methods in modern engineering system to investigate potential failure modes and its severity upon the system. Although there are several FMECA standards, the most popular one is MIL-STD-1629A from the U.S. Department of Defense. While carrying out FMECA, experts evaluate the severity and criticality of each failure mode and visualize the risk level matrix putting those indices to column and row variable, respectively. This kind of subjective evaluation inevitably brings ambiguity on the result. In order to handle the ambiguity, this paper proposes a new FMECA procedure using proposed structural importance (PSI) and fuzzy theory. The severity is evaluated by structural importance called PSI using minimal cut set objectively, while the criticality is determined by failure rate. Finally, the risk level is compounded of these indices by fuzzy expert system in order to resolve the ambiguity of traditional methods.

A preliminary Failure Modes, Effects and Criticality Analysis (FMECA) for the BIPS Flight System (FS) was published as AiResearch Report 76-311709 dated January 12, 1976. The FMECA presented a thorough review of the conceptual BIPS FS to identify areas of concern and activities necessary to avoid premature failures. In order to assure that the actions recommended by the FMECA are effected in both the FS and the Ground Demonstration System (GDS), a checklist (the BIPS-DIC) was prepared for the probability of occurrence of those failure modes that rated highest in criticality ranking. This checklist was circulated as an attachment to AiResearch Coordination Memo No. BIPS-GDS-A0106 dated January 23, 1976. The Brayton Isotope Power System-Design Integrity Checklist (BIPS-DIC) has been revised and is presented. Additional entries have been added that reference failure modes determined to rank highest in criticality ranking. The checklist will be updated periodically.

The paper focuses on the current engineering practice on SMART (System Safety, Maintainability, Availability, Reliability, and Testability) engineering using Failure Modes, Effects and Criticality Analysis (FMECA) as the fundamental SMART knowledge base. The paper demonstrates General Atomics Aeronautical Systems' adaptation to the Department Of Defense's (DOD's) new Reliability and Maintainability (RAM) engineering trend, such as quantitative hazard analysis, Reliability-Centered Maintenance (RCM) analysis, and fault coverage analysis, using the traditional RAM tool — FMECA.

Risk assessment is a systematic method of identifying and preventing product design and production planning process problems. It is a critical step in the risk management procedure. Most current risk assessment tools use failure mode, effects, and criticality analysis (FMECA) to evaluate the risk of failure. The traditional FMECA method uses linguistic terms to represent the severity, occurrence, and detection on a numerical scale from 1 to 10. It cannot deal with the incomplete information of attribute data. In a high technology industry, in order to meet the market’s real demand and provide a suitable product, an original design manufacture (ODM) company offers customized design, production, and integration service to satisfied clients’ needs. However, there are many uncertainties, imprecise, vague, and subjective information in the process of collecting data of an ODM company that will increase the difficulty of risk assessment. It cannot be fully solved by the traditional FMECA method. Therefore, considering both tangible and intangible criteria, this paper proposes a soft technique for order preference by similarity to ideal solution (TOPSIS) approach to solve the risk assessment problem. At last, a case study of ODM notebook module development is provided to demonstrate this practicability and rationality of the proposed method under incomplete information. Compared with existing traditional RPN and TOPSIS methods, the proposed method can deal with group decision-making problems in a more efficient manner.

Subsea storage is one of main technology in "Subsea Factory" concept that could significantly lower down the field life cycle cost especially in subsea development. An established qualification approach should enable Project Management Team (PMT) to evaluate the technology maturity, aims to reduce significant availability impact during deployment and operational phase. This paper describes the qualification approach adopted to qualify the subsea storage technology to achieve technology readiness level (TRL) 4 as per API 17Q framework, prior to pilot testing at offshore installation. One of important step is to evaluates whether the subsystem and critical components of subsea storage has an impact on Design, Operations and Maintenance. In this context, the evaluation considers the credible failure mode, mechanism, cause, and effect in relation to Risk Priority Number (RPN). Failure Mode, Effect and Criticality Analysis (FMECA) has been applied to evaluate the subsystem and critical component. The higher RPN indicates that the susceptible subsystem and critical components require special attention for improvement. The results pointed out that material for both top and bottom centre pipe flanges should have an ability to operate in cyclical loading and suitable for inspection, maintenance, and replacement program. These results suggest that recommended method of FMECA can be adopted during TRL assessment. Focus also should be on unlisted components which are not part of this assessment including design adequacy and stringent quality assurance and control management system, operations integrity, and maintenance comprehensiveness.

Risk assessment is a critical step in the roadmap of medical device development (MDD). Failure modes, effects and criticality analysis is a common approach based on declarative prior information that proved beneficial in the risk assessment of well-established processes. But at early steps of development when innovative materials or technologies are embedded, the lack of experience on those innovations introduces too much subjectivity in failure modes, effects, and criticality analysis (FMECA) for a robust risk assessment. Since mid-2000, the quality-by-design (QbD) guideline has been proposed within the pharmaceutical industry as a pro-active engineering approach to drug development. This paradigm enables a data-driven risk assessment throughout the development workflow, which completes the risk assessment provided by FMECA. Nevertheless, its implementation guide is unclear and not flexible enough to be efficiently applied to the development of medical devices. To address this issue, a new quality by design (QbD) paradigm indexed on the technological readiness level of the innovative product is proposed. It covers the development of medical devices throughout the whole preclinical phase and is composed of at least nine learning cycles. The first part of this medical device QbD layout, composed of three consecutive risk assessment cycles, is evaluated through a real study case with the objective to demonstrate the proof of concept of a photobleaching controller in photodynamic therapy. Beyond this experimental result, this application has confirmed practical ability of the indexed quality by design (iQbD) approach to complete FMECA and to provide an alternative solution to risk assessment when prior knowledge on the technological innovation is not available.

In HRC production at PT XYZ which is carried out continuously to pursue the production target will be in effect on the HRC production component with the risk management methods is Failure Mode, Effect and Criticality Analysis (FMECA). The purpose of this study is to identify the source of potential failure in HRC production, analyze the RPN value, and determine control recommendations. Data collection methods are carried out by means of direct observation of HRC production, interviews, and literature studies. The results on the risk assessment by the FMECA method are leakage and operational failure of the burner device with a RPN value of 420, the Refractory Brick Furnace crack of 324, the scale piece bounced off the 294 reservoir, the oil leak on the flat pass, the work roll, and the backup roll of 252 , damage to the roller table machine is 240, operational failure on the Looper engine of 240, and the failure of the HRC product binding manually is 240. Risk control in HRC production is the use of flame rod indicators, ultraviolet sensors, ultrasonic tests, installation of slab pieces cover, thermal infrared camera, LOTO use, quadratic linear inverse system or predictive control, distance guard, and periodic maintenance.