Improving Failure Analysis E ciency Of Handling Equipment By Using FMECA


 This work is part of an applied research study on the most commonly used methods in the industry: Failure Mode, Effects and Criticality Analysis (FMECA). In this study, the method was applied more precisely in an industrial production environment in a company responsible for producing and marketing various materials for public works and construction. During the elaboration of a preventive maintenance program for one of its equipment, (handling equipment) newly installed “the Pellegrini 10T overhead crane”, the company noticed that it is very important to understand precisely its behavior in case of failure to prevent its dysfunction or that of one of its components. This study aims to reduce the potential failures of handling equipment in the industry by using FMECA, which will help avoid potential risks and reduce downtime.

Therefore, in this study, a specialized FMECA procedure for material handling equipment was proposed by analyzing several FMECA speci cations used in other industries and considering the characteristics and requirements of material handling equipment. The proposed procedure has been applied on a 10T rolling bridge, which is a essential equipment for the production, in the compactor assembly workshops of an Algerian company, to establish a means of diagnosis and to bring improvements to its operation and this can be done only by controlling the weak and critical points of the rolling bridge on which it is necessary to act to optimize its availability First, we determined the criticality levels of the subassemblies of this rolling bridge after identifying the potential causes of failure in a second step. Then, we classi ed them according to their criticality to establish a plan of preventive and corrective actions that will de ne the maintenance policy to be undertaken to avoid the malfunction of the components of this system. Many scienti c works have dealt with this type of subject, and many problem-solving methods have been proposed. Among these methods is the FMEA, and its development and application is a subject of interest not only for academic researchers but also for industrial practitioners. Most design tools were created in industries to address speci c industrial problems. Analysis of a hypothetical accident in a two-unit power plant allowed Plastiras [19] to develop and apply a new methodology for analyzing common causes of inter-systems (ICCA), which revealed problems not identi ed by traditional methods FMEA of inter-systems previously performed by design teams. Dale and Shaw [20] conducted a questionnaire survey on the use of FMEA in the UK automotive industry. They found that most suppliers only started using FMEA because it was a contractual requirement of their customers; however, a number are now looking to make greater use of the technique to facilitate their quality improvement process. They also noted that organizations are not satis ed with the current FMEA training courses.
Aldridge et al. [21], during the application and design of FMEA at Garrett Automotive Ltd, Skelmersdale, and from an analysis of current methods of preparation and use of this technique, noted the reluctance of staff to product engineering and manufacturing engineering due to perceived lack of time or lack of understanding of process potential. Potente and Natrop [22] recommended the implementation of FMEA before the mass production of products to recognize errors that may occur during the manufacturing process, resulting in high scrap rates. The authors investigated that quality control was mainly performed by inspection of the nal product. Chabane et al. [23] contributed to implementing an approach dedicated to the behavioral analysis of industrial systems from the design itself. This approach is a combination of three complementary tools. The SysML language is applied to express customer needs and requirements, such as functions and operating conditions of future systems. In addition, the FMECA method analyzes potential system malfunctions and recommends appropriate maintenance actions.
Finally, the K-means method classi es the failure modes to obtain the detailed criticality of the modes instead of calculating them according to the old methods.
Similarly, combining the TOPSIS fuzzy belief method with FMEA to introduce an FMEA belief structure to describe expert knowledge by a several linguists as a grammatical phenomenon was proposed by Vahdani et al. [24]. Thivel et al. [25] analyzed the risk of a biomass combustion process using systematic methodological risk analysis and FMEA. FMEA has been used to study the reliability of many energy production systems. Arabian-Hoseynabadi et al. [26] applied it to a wind turbine system using a proprietary software reliability analysis tool. In a case study, Nazeri and Naderikia [27] proposed an approach for assessing and determining the risk of failure modes for a railway company. The authors aim to develop a risk-based method to select an appropriate maintenance strategy to have available and reliable tamping equipment in Iranian railroads. Raj Kumar and Jindal [28] applied the FMEA approach by evaluating the possible causes of failure and their effects on the sub-systems of CNC machines to improve their reliability in the industry and increase the performance of rate production through the prioritization of the failures occurring in them.
Since the risk priority number (RPN) calculation method is one of the critical subjects of failure mode and effects analysis (FMEA) research, Xiaojun and Jing [29] recently proposed a fuzzy beta-binomial RPN evaluation method by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution. Early product defects have plagued many machine tool enterprises, especially small and medium-sized enterprises limited by capital. Against this background and to eliminate early defects and improve the reliability of computerized numerical control (CNC) machine tools, Zhang et al. [30] proposed a systematic early defect elimination method based on the after-sale data (customer eld data) of CNC machine tools. The proposed method consists of four steps: defect data collection; a four-parameter non-homogeneous Poisson process model (NHPP); a mixed defect analysis method that is the combination of the fault tree analysis (FTA) method and the failure modes, effects, and criticality analysis (FMECA) method; and early defect elimination measures.
Other researchers have obtained useful results in improving performance in various industries and avoiding failure by combining FMEA with other improvement methods. For example, Almannai et al. [31] described an integrated approach to help management address technology, organization, and people at the earliest decision-making stages regarding manufacturing automation. The approach used the Quality Function Deployment (QFD) and the FMEA techniques. The key concepts from these two applications were merged into a decision tool; QFD identi es the most appropriate manufacturing automation alternative, and FMEA identi es the risks associated with that option to be considered in the design and implementation phases of the manufacturing system.
On the other hand, Peeters et al. [11] proposed a method to perform failure analysis by combining the Fault Tree Analysis (FTA) method and the Failure Mode and Effects Analysis (FMEA) method. The authors reported that the main disadvantage of applying these two methods is time-consuming. As a result, the methods are often not applied thoroughly, leading to failure to identify important failure modes. Furthermore, Bertsche [32] stated that this could increase the number of failure modes found due to the different starting points of the two methods: bottom-up in FMEA versus top-down in FTA. Fault tree analysis (FTA) and fault mode and effects analysis (FMEA) approaches are widely used for fault analysis. However, they are time-consuming and expensive when fully implemented and can also lead to a loss of focus on the most critical parts of the system, which failure analysis is generally aimed at identifying. Implementation of food safety management systems (ISO 22000:2018), as well as the incorporation of management tools such as HAZOP, FMEA, Ishikawa, and Pareto, were found to be proactive in maintaining a positive food safety culture and preventing cross-contamination and fraud in a case study by Lee et al. [33]. The FMEA model was too applied for the risk assessment of the pastries processing [34]. Tra alek and Kolanowski [35] used FMEA for auditing the HACCP system. The researchers showed that the designed method is ready to be used in all types of food enterprises. In addition, FMEA has been used to analyze and reduce risks (increase safety) in other areas such as industries. Papadopoulos et al.
[36] automated failure modes and safety effects analysis of critical systems.
Arvanitoyannis and Savelides [37] implemented an interim FMEA approach in an industry producing lled chocolate to exclude the presence of genetically modi ed organisms (GMOs) in the nal product. They used two structured methods (preliminary risk analysis and fault tree analysis) to analyze and predict the failure modes in the food chain system, based on the functions, characteristics, and/or interactions of the ingredients or processes on which the system depends. Aljazzar et al.
[38] used FMEA and probabilistic counterexamples for the airbag system in the healthcare sector. In addition, FMEA can be used to increase patient safety in hospitals, Chiozza and Ponzetti [39] used it to reduce medical errors.

Methodology
FMEA was rst developed to address reliability and safety issues in the aerospace industry in the late 1950s. Because FMEA focuses on proactive prevention of potential failures rather than solutions. It can help managers identify failures and causes/effects and eliminate failures by instituting corrective actions in the risk assessment process [40]. There are several types of FMEA/FMECA: -Design FMEA (DFMEA): It is used to ensure the reliability of a product by improving its design.
-Process FMEA (PFMEA): It ensures the quality of a product by improving its production operations.
-Concept FMEA (CFMEA): It ensures the availability and safety of production equipment by improving its maintenance. Therefore, to perform an FMEA/FMECA, it is necessary to know the system's operation, process, or product being analyzed or, failing that, to have the means to obtain the information from those who have it. The FMEA/FMECA is a "step-by-step" procedure (a sequence of questions and answers) that involves, in the rst instance, a tabulation of the system's functions or the system's equipment items, the failure mode of each item, and the effects of the failures on the system: 1. Establish the FMEA/FMECA team.
2. Describe the product/process/system you want to analyze. 3. Creating a Block Diagram of the product or process that shows the major components or process steps as blocks connected by lines that indicate how the components or steps are related. 4. List of Potential failure modes, causes of failure, and their effects on the system. 5. Assign Severity, Occurrence, and Detection rankings to each failure mode (Tables 1, 2, and 3).
6. Calculate the RPN (Risk Priority Number) using the mathematical formula (RPN= Severity X Occurrence X Detection) ( Table 4). The results of an FMEA/FMECA are typically recorded in a table, such as the table shown in Figure 1. 7. Develop the action plan and De ne who will do what and when. 8. Take the actions identi ed by your FMEA/FMECA team. 9. Calculate the resulting RPN after implementation of the actions. 10. Compare RPN before and after implementing the actions and Reassess each of the potential failures after improvements are made, and determine the impact of the improvements using the FMEA/FMECA. Table 1 Severity of failure effects (S).

Level
Value De nition Minor 1 Failure stops the component but not the installation that operates in degraded mode.
Way 2 Failure stops the equipment but not the production that operates in degraded mode.

Major 3
Failure stops the production from 20 min to 1 h and requires maintenance.
Failure stops the production for more than 2 h.
Important intervention on the sub-systems.

Catastrophic 5
Failure stops the production, leading to serious problems for personnel safety or the installation/environment.
Heavy intervention requiring knife equipment.

Value
De nition 1 < C < 8 Negligible: we leave them aside.

< C < 14
Average: we wonder whether to leave them or keep them.
14 < C < 27 High: we need to nd actions to implement and the importance of stocking components or organs.

< C < 64
Prohibited: the actions must be recorded, and the components or parts must be put in stock.

Context
The case study was carried out in an Algerian company producing and marketing various materials for public works and construction. The methodology was applied to one of its handling equipment newly installed in the assembly workshops: Pellegrini 10T overhead crane ( Figure 2). It was designed to lift and move heavy parts for the assembly of compactors. Table 5 lists the characteristics of this system.

Results and Discussion
After identifying all failure effects and the root-cause analysis of the failure modes in the teamwork, all corresponding RPN values were calculated (Table 6).
a Table 6 Analysis of failure modes, their effects, and criticality (FMECA). The criticality hierarchy can be formalized as a histogram (Figure 3). The criticality threshold is set at 10 by the working group's proposals. This threshold is the limit beyond which preventive actions must be taken. It is also possible to draw up a list of critical points or a table of Corrective/Preventive actions that must be taken on the elements of the Pellegrini10T overhead crane (Table 7), having risks of critical failures. A reduction in criticality can be achieved by acting on one or more factors of the product, as follows:

Subsystem
To propose preventive actions to the anomalies detected by the FMECA study, we based ourselves on the history of failures of overhead cranes similar to our system and existing in the company and on the manufacturer's manual. b Table 7 Corrective/Preventive actions on the Pellegrini 10T overhead crane sub-systems.  Actions: -It is strongly recommended to inspect it regularly (required by the manufacturer).

Daily
Bearing surfaces 12 Interpretations: Worn, abraded, or oxidized surfaces can represent a progressive degradation accelerator of the various components of the equipment (system). Actions: -Knowledge of the type of materials used for this component is strongly recommended.
-Control by vibrometer or accelerometer is recommended.

Beam 15
Interpretations: The potential risks of improper beam assembly and the risks associated with machines such as cracks propagating through vibration and overload are considerable. Actions: -It is strongly recommended to inspect them regularly even if the frequency of occurrence of its problems is rare because the consequences are serious on equipment and the personnel.
-Visual inspection with appropriate light is su cient.

Mechanical braking system 16
Interpretations: This hydrodynamic system, even if it is not frequent to break down, but its effects are catastrophic, which justi es its criticality value, so a daily vacuum control is mandatory knowing that it only takes two minutes. Actions: - Interpretations: This organ is a parallelepiped container xed to a beam. It gathers and protects the electrical components of the system. The failure of one of its components affects the operation of the machine. Each 6 months c The implementation of such a preventive maintenance plan makes it possible to prevent and reduce the interruption of production operations, to maintain the equipment in such a condition that it can operate e ciently and to ensure the quality of the service to be provided or that of the nished product.

Conclusions
The objective of this study was to determine how to achieve maximum equipment e ciency and high industrial performance. A cost-effective maintenance strategy applicable to strategic equipment was applied to the machinery park of an Algerian company on the Pellegrini 10T rolling crane, operating in the compactor assembly workshops. This maintenance strategy optimized the maintenance team's costs, time, and effort.
Studies have shown the feasibility of implementing a maintenance optimization method as the in uence of maintenance activities becomes more and more important in the management of companies. The method used in this study is the FMECA. It is one of the tools for continuous improvement. In addition, it allows quality to be achieved through preventive rather than curative action.
The FMECA was carried out in four stages: • Preparation • Functional decomposition • Analysis phase

• Establishment and monitoring of action plans
The results obtained allow us to prioritize the potential causes of the failures identi ed, target the most critical, and propose a maintenance plan-based on preventive/corrective operations to improve and optimize the machine's performance and ensure its availability. Figure 1 FMEA/FMECA spreadsheet.

Figure 3
Hierarchical histogram of the Pellegrini 10T overhead crane sub-systems criticality.