Deployment of Reliability Centered Maintenance Methodology On a Machine To Avoid Unscheduled Downtime

Transcription

1 Deployment of Reliability Centered Maintenance Methodology On a Machine To Avoid Unscheduled Downtime Laxman Pangeni*, Amandeep Nagpal**, Sridhar Krishnaswamy*** Eaton Technologies Private Limited, Pune, , India * Laxman Pangeni. Tel: , LaxmanPangeni@Eaton.com **Amandeep Nagpal. Tel: , AmandeepNagpal@Eaton.com *** Sridhar Krishnaswamy. Tel: , SridharKrishnaswamy@Eaton.com Abstract Reliability Centered Maintenance (RCM) is a structured framework that helps to identify appropriate and effective maintenance strategies to arrest or retire the failure modes that can affect the functioning of a system. It could be used to optimize or to achieve improvements in existing maintenance strategies. This paper presents a case study on the deployment of RCM principles on a machine to develop reliability driven maintenance strategies. The machine is used in manufacturing prototypes of products under development, thus any breakdowns would cause increase in time to market for the product release. Generally, any machine observes the combination of corrective and holistic preventive maintenance strategies which along with ineffective forecasting of spare part requirements could lead to untimely breakdown of its critical components. This further leads to low availability. The machine under consideration consists of the combination of mechanical, electrical and electronic components. RCM deployment helped in optimizing the existing maintenance program as well as bringing new failure mode and effects analysis (FMEA) driven design improvements and maintenance strategies. These strategies included condition based as well as restoration/discard procedures which helped in avoiding the unscheduled maintenance activity. Post implementation of RCM, the machine observed significant increase in availability in the last six month monitoring period without any major breakdown. In conclusion, RCM methodology improved the machine availability by arresting or retiring major failure modes pertaining to the critical or significant components. This was done by identifying effective maintenance strategies for each failure modes as well as identifying optimized spare part requirements. Keywords: Reliability Centered Maintenance (RCM), Failure Mode and Effect Analysis (FMEA), Machine I.INTRODUCTION Machine considered for the RCM assessment and deployment is one of the most critical assets looking at its high utilization. The machine is used for manufacturing prototypes of different products under development as per the standards or customer requirements. These products under development are worth huge market potential. The delay in manufacturing prototypes by even a week results into the huge loss of this potential market. The globalization of the products requires the increase in the quantity and varieties of the prototypes depending upon the market or the region the product targets. This results in an increase in demand for prototype building observed by the machine (see Figure 1) over the years. Thus the higher availability of the machine is and will become much more important in the future. Any downtime of the machine because of unavailability of the spare parts or because of the breakdown of the critical components would cause increased product manufacturing completion time and thus delay in on-time availability of the product to the customers. 1

2 Prototype Demands This paper presents a case study of the RCM methodology and the deployment initiative taken to develop maintenance and spare part strategy for a prototype manufacturing machine. With the help of the methodology one should be able to increase the availability of the machine # of Prototype Requirements 20 0 Yr-1 Yr-2 Yr-3 Yr-4* Year * Forecasted Figure 1. Increase in demand for prototype manufacturing II.ABBREVIATIONS AND ACRONYMS CM: Condition Monitoring FF: Failure Finding FM: Failure Mode FMEA: Failure Mode and Effects Analysis MTBF: Mean Time Between Failures MTTR: Mean Time To Repair RCM: Reliability Centered Maintenance RD: Restoration or Discard RTF: Run To Failure III.RELIABILITY CENTERED MAINTENANCE METHODOLOGY RCM is a structured approach to establish maintenance requirements of any assets (plant, assembly line, process, product, etc.). It helps to minimize or retire the key failure mode (FM) that can affect the functioning of any system under consideration. 2

3 Identification of system for RCM assessment Identification of critical components Perform FMEA RCM Assessment Deliverables Data collection and analysis System Identification System Structuring Identifying all functions for each components Data collection and analysis Identification of critical components of the system under consideration Identification of functional failures & compensating provisions of each functions for critical components Identification of failure modes and their effects. Prioritize each failures based on evident or hidden criticality. Develop maintenance tasks for each failures. Develop maintenance strategies based on the identified tasks. Develop spare part requirements based on the identified tasks. Deploy the RCM driven maintenance strategies. Monitor the availability. Figure 2. RCM process flow RCM philosophy includes the mix of condition monitoring (CM), restoration or discards (RD), failure finding (FF) and run to failure (RTF) procedures [1]. These procedures are selected based on functional failures of any components such that the component will have increased reliability with minimum performance of maintenance. As can be seen in the Figure 2, the RCM assessment steps are shown with key deliverables [2]. The steps include structured framework for RCM deployment. First of all, RCM plan is developed by identifying the critical system through past availability data for each system. The identified system is structured based on subsystems and components. Each function of individual components is identified further. Finally the critical components are identified based on historical failure data - Mean Time Between Failure (MTBF) and engineering judgment. The second step involves performing Failure Model and Effects Analysis (FMEA) for each function of the critical components. FMs, their causes and their effects for each function are identified. Severity for each FM is assigned. The FMEA helps to identify the critical FM that needs to be arrested or retired to preserve functions of the system. FMEA is thus one of the critical steps of the RCM assessment. The next step involves assigning the maintenance tasks for each FM. Each FM is prioritized based on its impact on safety, environment or economy and also based on whether the FM is evident or hidden to the operator. This further helps to identify the combination of maintenance procedures based on the available information as shown in Figure 3 [3]. 3

4 % Availability RCM Maintenance Procedures Condition Monitoring (CM) Procedure Restoration or Discard (RD) Procedure Failure Finding (FF) Procedure Run To Failure (RTF) Procedure Known failure trends Technically feasible to determine whether a failure is occurring or about to occur. Replacement time known Technically feasible to reduce failure frequency through scheduled restoration or discard procedure. Technically feasible to detect a failure. FM criticality is acceptable. The final step of RCM assessment involves deployment of RCM at the ground level. This involves preparing maintenance strategy check sheet and spare part list. Figure 3. RCM maintenance strategy selection criteria IV.CASE STUDY A machine is selected for the present work based on its high utilization and poor availability. The machine is used for prototype manufacturing of upcoming products and has the average availability of ~60-70% over the last four years (see Figure 4). 75% 71% 70% 65% 63% 62% 60% 55% 57% % Availability 50% Yr-1 Yr-2 Yr-3 Yr-4 Year Figure 4. Machine availability year-on-year A. System under consideration The machine under consideration consists of eight sub-systems. The sub-systems of the machine are shown in Figure 5. 4

5 Average Failure Count per Year Cumulative % Sub-System 7 Sub-System 7 Cool Liquid Sub-System 4 Sub-System 3 Water Hot Liquid Sample Prototype Sub-System 2 Sub-System 1 Sub-System 5 Sub-System 6 Figure 5. Sub-systems of the Machine B. Identification of critical components - Analysis of last three years failure data of the machine shows that the components 1, 2 and 3 are the critical components, of different sub-systems, that have highest number of average failure counts per year (see Figure 6). These components have 796 hrs. 876 hrs. and 1460 hrs. of MTBF respectively. Thus, these three units have been considered for further assessment. Since the failure of the components 4, 5, 6 & 7 are negligible and these being low cost items, these components will be run to failure and no prior maintenance will be done for the same. Analysis of Test Stand Component Failures % 94% 97% 100% 100% 120% 100% % 6 80% 80% 60% No. of Failures % % 20% 0% Cumulative % 80% Ref Line Figure 6. Failure analysis of the machine components C. Performing FMEA FMEA is one of the key steps of the RCM methodology that differentiates the RCM driven maintenance strategies with the conventional maintenance strategies. FMEA is performed on the identified critical components. Top row of the Figure 7 shows the key buckets of FMEA. Historical failure data are taken as an input to identify the key FMs of the individual critical components. Root cause analysis is performed on the components to identify the key causes leading 5

6 Increasing severity rating to the FM. The end effect of these causes are then identified and mentioned against each causes. Based on whether the end effect creates no visible effect to the customer or whether the end effect results in violation of safety requirements, each end effect is assigned a severity rating as shown in Figure 7 and Figure 8. S. No. Components Function FM Cause End Effect Severity Detection Compensating Provisions Spare Part Requirements 1 Component 1 Performs a function No function 1. Piece to piece variation 2. Unfavourable external environment Loss of primary function 8 Detection through sensors. Cleaning. - Partial function 1. Unfavourable external environment 2. Improper system interactions Degradation of primary function 7 Detection through sensors. Cleaning. - Over function 1. Piece to piece variation Annoyance to minor customers 2 Detection through audible abnormal Replacement. noise. 01 qty. 1. Degradation over time Time-degraded function 2. Unintended customer usage Degradation of primary function 7 No detection in place. Repairing. - Intermittent function 1. Improper system interactions Annoyance to major customers 4 Detection through sensors. Cleaning. - Unintended function 1. Unintended customer usage Affects safe operation 9 Detection through sensors. Replacement. 01 qty. Figure 7. FMEA performed on the critical components Figure 7 shows a snapshot of FMEA performed on a critical component (Component 1) of the machine. It also shows the detection tool and compensating provisions available for each FM. For the FMs where detection is not possible, CM or FF maintenance procedures may be used for detectability. SR 10 SR 5 Failure to meet safety and/or regulatory requirements Degradation of primary function Degradation of secondary function SR 1 Causes annoyance to the customer No visible effect Figure 8. Criteria to assign severity rating D. RCM Assessment This is the final step before the actual deployment of RCM happens. RCM assessment involves prioritization of FMs based on whether the FM is hidden or evident to the operator working on the machine. This is achieved through the criticality and detectability assessed during FMEA stage. Further, the maintenance tasks are identified for each FMs based on maintenance strategy selection criteria as shown in Figure 3. The snapshot of identified maintenance strategies and the frequencies for component 1 are shown in Figure 9. 6

7 S. No. Components Function FM Detection Identified Maintenance Strategies Frequency 1 Component 1 Performs a function No function Detection through sensors. Combinations of FF and RD Partial function Detection through sensors. Combinations of FF and CM Over function Time-degraded function Detection through audible abnormal noise. No detection in place. Combinations of FF and RD FF and CM Intermittent function Detection through sensors. FF Quarterly Unintended function Detection through sensors. FF Quarterly FF - Daily RD - Quarterly and Annualy FF - Daily CM - Monthly FF - Daily RD - Quarterly and Annualy FF - Daily and Quarterly CM - Weekly Figure 9. Maintenance tasks identification for each FM E. Spare Parts Stock Control and Maintenance The spare part stock requirements and maintenance of each component is finalized through the data gathered from the RCM assessment. This is estimated based on shelf life of the component which defines the time for procurement initiation, the causes of the failure mode, detectability possible for the component (refer Figure 7), the quantity of the component used for the system, the redundancy available in place when the component fails, the MTBF and the MTTR data available for the component. Since the shelf life of components 1, 2 and 3 are 10 years, 3 years and 20 years respectively, which is much higher than MTBF, the shelf life could be neglected for the estimation of spare part stock for the system under consideration. The spare part requirements based on the failure trend (Figure 6) suggest maintaining the spare stock equal to the number of breakdowns observed for each component. However, based on the FMEA performed and the maintenance strategies identified through RCM assessment, the spare part requirements for the critical components is optimized as mentioned in Figure 7. This could be further optimized if the redundancy is available in place for these components and if the MTBF data is available post RCM assessment which is collected from the day RCM is deployed. F. Deliverables The final steps of the RCM assessment is the release of maintenance strategy checklist and spare part lists to monitor the impact of the identified maintenance strategies and spare part lists on reducing the machine down time. V.RESULTS Further, post RCM deployment, the machine has been monitored for 6 months (Q1 and Q2 of the present year). The result shows improvement in availability from existing 60-70% to 90-95% (refer Figure 10). Considering 600 hours of machine utilization per month, with 90-95% availability, we have been able to reduce average per month breakdown 7

8 % Availability significantly. This reduction in the breakdown helped to complete the prototype manufacturing of a product well before the required time. This further helped in on-time delivery of the product to customers, thereby providing a huge business opportunity. 100% 95% 93% 95% 90% 85% 80% 75% 71% 70% 65% 60% 57% 63% 62% % Availability 55% 50% Yr-1 Yr-2 Yr-3 Yr-4 Q1 Yr-5 Year Q2 Yr-5 Figure 10. Availability trend of the machine VI. CONCLUSION In this paper, a case study is shown for the deployment of RCM on a prototype manufacturing machine. RCM preserves the function of critical components by arresting or retiring the key FMs. This methodology would improve the organization asset utilization by reducing the frequency of untimely failures. It shall also help in cost out through optimization of existing maintenance strategies and the spare parts as per the requirements. VII. FUTURE WORK In this work, the past failure data is taken as a key input to identify the critical components of the machine under consideration. This approach could be used for existing assets. To implement the RCM methodology on the assets where past data is not available, it is recommended to work on physics-of-failure driven relationships to identify the failure trend and the critical components. India). ACKNOWLEDGEMENT The authors gratefully acknowledge the technical support provided by Pravin Kadekodi (Engg. Manager, Eaton REFERENCES [1] Islam H. Afefy, Reliability-Centered Maintenance Methodology and Application: A Case Study, Engineering, 2, pp ,

9 [2] MSG-3, Maintenance programme document MSG-3 Revision 2, Air Trasnport Association of America, [3] J. Ashayeri, Development of computer-aided maintenance resources planning (CAMRP): A case of multiple CNC machining centers, Robotics and Computer-Integrated Manufacturing, 23, pp ,