ANALYSIS OF FAILURE STATISTICS FOR CONE CRUSHER MAINTENANCE Vis-a-Vis OPERATIONAL RELIABILITY ASSESSMENT

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1 International Journal of Mechanical and Production Engineering Research and Development(IJMPERD) ISSN Vol. 3, Issue 1, Mar 2013, TJPRC Pvt. Ltd. ANALYSIS OF FAILURE STATISTICS FOR CONE CRUSHER MAINTENANCE Vis-a-Vis OPERATIONAL RELIABILITY ASSESSMENT R. S. SINHA 1 & A. K. MUKHOPADHYAY 2 1 Research Scholar, Department of Mechanical Engineering and Mining Machinery Engineering, Indian School of Mines, Dhanbad, Jharkhand, India 2 Professor, Department of Mechanical Engineering and Mining Machinery Engineering, Indian School of Mines, Dhanbad, Jharkhand, India ABSTRACT A cone crusher is used for fragmenting rock up to few millimetres. The vast majority of these equipments are found in mineral processing plants. The random component failures of the crusher have considerable influence on productivity of the plant. In order to restore its performance and operational reliability, its critical components are to be identified first to make replacement in time. Traditional maintenance practices are prevailing in the plant. A scientific analysis of failure statistics found imperative to improve the operational reliability of this critical equipment. The present paper elucidates the methods of failure analysis for improving its operational reliability. Reliability tools - FMEA (Failure Modes and Effects Analysis), Risk Priority Number (RPN) and TTT-plot (Total Time to Test Plot) are explored in this study. The failure data of one year is used in the analysis. The goal is to carry out statistical analysis of failure data to capture the benefits of common sense reasoning about criticality of the equipment component, its malfunction and reducing downtime of the equipment by taking appropriate and timely preventive measure. KEYWORDS: Risk Priority Number (RPN), TTT-Plot (Total Time to Test Plot), FMEA (Failure Modes and Effects Analysis), Crusher INTRODUCTION The supply of minerals, ores and aggregates are crucial for the continuous development of today s society. The reliability of crusher in a crushing plant plays a major role to meet the escalating demand. Any failure of crusher component may stop functioning of the plant. Different maintenance approaches are in practice in the plant, each one is used under different circumstances and conditions, all related to the specific demand of the plant operation. Each one of the methodologies demands to improve reliability. This study evaluates the risk associated with crusher components and to prioritize maintenance actions. Reliability tools - FMEA (Failure Modes and Effects Analysis), RPN indexes and TTT-plot are considered here. For condition evaluation, frequency values of failures, magnitude identification of each failure, developing criteria for quantification of reliability of the component and allotted risk priority number (RPN) related to each component failure have been considered. A maintenance model is designed that allows the prevention, detection and correction of problems. The model will contribute to improve the maintenance indicators and based on the consequences, decisions may be taken to promote the plant s maintenance strategy and improving crusher reliability. THE EQUIPMENT Cone crushers are extensively used for rock breakage and as a secondary crusher or tertiary crusher in a crushing circuit. The different parts of a cone crusher are bowl, head, eccentric assembly, liners etc. The drive shaft rotates the head eccentrically, compressing rock between the head and crushing chamber and simultaneously discharges crushed product

2 126 R. S. Sinha & A. K. Mukhopadhyay from its bottom discharge zone. Power is transmitted from the electric motor to the shaft through V-belt drive arrangement. The head is backed with plastic cement, zinc, or more recently with an epoxy resin. The throw of cone crushers is larger than that of primary crushers, to withstand heavier working stresses. The bowl can be held down either by an annular arrangement of springs or by a hydraulic mechanism. Figure 1 presents a simplified view of a cone crusher. Figure 1: A Cone Crusher FMEA, RPN AND TTT-PLOT Failure modes and effects (FMEA) analysis is a popular tool for reliability and failure-mode analysis. It identifies, first, the potential failure modes of a machine component during its life cycle; second, the effects of these failures; and, third, the criticality of these failure effects in component functionality. In the FMEA process, component functions are carefully evaluated, and the potential failures are listed. It is an effective tool in reliability prediction. The risk priority number (RPN) methodology is a technique for analysing the risk associated with potential problems identified during FMEA. The TTT-plot is used to analyse the distribution characteristics of component failures. METHODOLOGY FMEA and RPN Analysis FMEA is a methodology to evaluate failure modes and their effects in the system improvement investigations. Proper identification of failure may lead to solutions that improve the operational reliability of a system. In this study FMEA is carried out in two stages with total twenty number of critical failures occurred in one During the first stage, possible failure mode of crusher components and its prejudicial effects have been identified. In the second phase, based on FMEA, the critical level (or risk score) of each failure is determined. The most critical failures head the ranking and therefore to be considered first during corrective actions to minimise the likelihood of them occurring. All the information on component failure is collected from the maintenance record book of the plant. Table 1 and Table 2 shows the headings of the FMEA report prepared in this exercise.

3 Analysis of Failure Statistics for Cone Crusher Maintenance Vis-a-Vis Operational Reliability Assessment 127 The columns of the tables deal with failure type, corrective actions, frequency of failures, effects of failure etc. related to the crusher unit. Ordering of indices of different aspects based on experience is presented in Table 3 and ordered as per ordinal-scale data. The value 1is assigned to the lowest ranked category in the continuum, then 1 is below 2, 2 is below 3, 3 is below, and so on. The index for severity reflects the gravity of the failure consequences. The index for occurrence defines the rate of failure. The index for detection relates to the possibility of detecting a failure in advance. Table 1: Crusher Component Failure Statistics Sr. No. Failure Type Maintenance Action No. of Failures in a Year 1 Bowl liner wear Liner changed 6 2 Head liner wear Liner changed 7 3 Dust seal damage Dust seal changed 2 pipe damage contamination pipe change change 2 3 Average Time to Repair Table 2: Cone Crusher Component Failure Detail Description of System Functions of the Crusher COMPONENT FUNCTIONAL FUNCTION OF MODE OF FAILURE OF COMPONENT FAILURE COMPONENT EFFECT OF FAILURE Bowl To Crush the ore Bowl damage Sudden impact Stops crushing Bowl liner To protect the bowl Bowl liner damage Wear and tear Cause deterioration in crushing performance and bawl damage Dust seal To protect the bearing from dust, dirt, moisture etc. Dust seal damage Wear Cause entry of dust and moisture into moving parts Head To break the rock Head damage Sudden impact Stops crushing Head liner pipe Lubricating oil To protect the crushing head Supply oil from oil tank to different parts Lubricates moving components Head liner damage Wear and tear Cause deterioration in crushing performance and head damage Oil pipe damage Pipe breakage Inadequate lubrication, subsequently cause failure to other components Loss of lubricating property Dust, dirt and moisture contamination Improper lubrication, subsequently cause failure to other components

4 128 R. S. Sinha & A. K. Mukhopadhyay Table 3: Ordering of Indexes with Different Aspects Index Severity of Problem 1 Very low, system remains in operation but needs corrective action 2 Very low, system remains in operation but needs corrective action 3 Low, system remains in operation but needs corrective action Low, system remains in operation but needs corrective action Moderately low, system remains in operation but needs corrective action 6 Moderately low, system remains in operation but needs corrective action 7 Hazardous with warning, system remains in operation but needs immediate corrective action 8 Hazardous with warning, system remains in operation but needs immediate corrective action 9 Hazardous with warning, system remains in operation but needs immediate corrective action 10 Hazardous without warning, system stops working, needs immediate corrective action Likelihood of Occurrence No failure or 1 failure in last 1 year 2 failures in last 1 3 failures in last 1 failures in last 1 failures in last 1 6 failures in last 1 7 failures in last 1 8 failures in last 1 9 failures in last 1 10 failures in last 1 Likelihood of Detection Very easy to detect Very easy to detect Easy to detect Easy to detect Moderately easy to detect Moderately easy to detect Moderately easy to detect Remote possibility to detect Remote possibility to detect Impossible to detect RPN is the practical way to prioritize certain failures and to evaluate which step is to be taken first. The Risk priority number (RPN) is used to rate each potential problem according to three rating scales. RPN is calculated by the product of the three indices: Severity x Occurrence x Detection. Severity rates the severity of the potential effect of the failure. Occurrence rates the likelihood that the failure occurred. Detection rates the difficulty in detecting the failure in advance. The study has assigned a rating of 1 to 10 on each scale, with 10 being severe, very likely to occur, and impossible to detect in advance. These three ratings are then multiplied together to obtain RPN value. These RPN values are then used to rank the problem. After obtaining RPN, a ranking of the causes of the failures is drawn up and is presented in Table. Finally, corrective action is to be taken by the maintenance group beginning with the riskiest as indicated by the highest RPN score. Table : Evaluated Risk Priority Number TTT-Plot Component Failure FACTORS FOR FAILURE MODE EVALUATION Severity Occurrence Detection RPN Bowl liner wear Dust seal damage Head liner wear 7 10 pipe damage contamination 3 7 In this study, TTT-plot is used to analyse rate of failures considering time between successive failures (TBFs). The times between successive failures are generally exponentially distributed random variables. In exponential distribution, the failure rate does not depend on time. This follows that the failure rate is constant. Further, this can be interpreted as a component does not age. Since this is often not the case for mechanical components like crushers, a graphical plot is more informative technique. TTT-plots are indicative of the increase or decrease in failure rate. It is used

5 Analysis of Failure Statistics for Cone Crusher Maintenance Vis-a-Vis Operational Reliability Assessment 129 to determine the failure rate and the need for maintenance of ageing components. To develop TTT-plot, following steps have been considered: TBF is structured as 0 <= t (0) <= t (1) <= t (2)...<= t (n), i = 1,2,...n S i is determined from S i = nt (1) +(n-1)(t (2) -t (1) ) (n-i+1)(t (i) -t (i-1) ), i = 1,2,...n X co-ordinate = (i/n) where i = 1,2,3...n Y co-ordinate = U i = S (i) /S (n) In above four steps, i is the number of failure and n is the number of observations for time between failures. Ui is the ratio of Si/Sn, where, Si is the TTT at time t i and Sn is the TTT at nth failure, where i=1, 2, 3..n. The values (i/n) and U i will lie between 0 and 1. The graphic representation TTT is formed by plotting i/n on the horizontal axis and Ui on the vertical axis. In general, the plot of the scaled time-on-test transform is within a unit square. The plotted points are connected by straight lines. For an exponential distribution, the TTT-plot is close to the diagonal of a square unit. The departure of the plot from the diagonal indicates the behaviour of the failure rate. The TTTplot runs along and nearly coincides with the diagonal of the square in case of constant failure rate. In case, the TTT plot is concave downwards, it suggests an increasing failure rate, that is, the condition of unit or the machine as a whole is deteriorating in terms of failure. If the plot concaves upward, it would suggest that failure rate is decreasing, that is, the condition of individual unit or the total machine is improving. Figure 2 explains the situations. (a)ttt-plot for Constant Failure Rate (b) TTT-Plot for Increasing Failure Rate (c) TTT-Plot for Decreasing Failure Rate Figure 2: TTT-Plots at Different Failure Rates The data for TTT-Plot of crusher unit under study is presented in Table and based on these data the TTT-plot of the crusher unit is shown in Figure 3.

6 130 R. S. Sinha & A. K. Mukhopadhyay Table : Data for TTT-Plot of Cone Crusher Where, Serial Number of Successive Failure (n) Time Before i th Successive Failure (TBF) S i =(TBF) i (n) U i = (S i /S n ) i/ n i= i th number of successive failure n= Number of successive failures and total 20 successive failures have been considered in this study S i = total time on test at i th successive failure S n = total time on test at all successive failures under study and is equal to 288 in this case Figure 3: TTT-Plot of Cone Crusher

7 Analysis of Failure Statistics for Cone Crusher Maintenance Vis-a-Vis Operational Reliability Assessment 131 DISCUSSIONS The logical steps to be followed in FMEA exercise is presented in Table 1 and Table 2. These two tables list main components of the crusher, function of each component, number of failures, mode of failure, time to repair a particular fault and the effect of failure. Based on observed data, it could be concluded that the critical components of the crusher are bowl liner, mantle liner, dust seal, lubricating pipe and the lubricating oil. FMEA exercise helped in determining the severity of failures. It prioritises the maintenance decision that should be taken to reduce risk based on RPN index. The classification criteria of severity, likelihood of occurrence and likelihood of detection is presented in Table 3. The RPN values then calculated by the product of the three indexes which are shown in Table. The RPN score is highest for head liner wear followed by bowl liner wear, dust seal damage, lubrication pipe damage and contamination in lubricating oil. It is a practical way to prioritize failures and to evaluate which step is to be taken first. The spread sheet of analysis of the functions - number of successive failures, time before successive failures, S i, U i and i/ n is shown in Table for TTT-plot. It is used to obtain the shape of the failure rate function indicating the failure rates and aging of components and decides the duration of optimal preventive maintenance. CONCLUSIONS The accumulation of knowledge through FMEA, RPN and TTT-plot would be an effective method to suggest preventive maintenance schedule with an aim to improve operational reliability of the crusher unit. FMEA is used as a tool to systematically identify and evaluate the effects of different component failure modes, to determine what could eliminate or reduce the chance of failure, and to document the system under consideration. RPN prioritizes failures and maintenance actions. RPN values should be re-evaluated time to time to see if they have gone down to check the efficiency of the corrective action for each failure cause. The TTT-plot examines whether the maintenance of the cone crusher is satisfactory or not. If the maintenance of the equipment is satisfactory then preventive maintenance of the crusher could reduce the maintenance costs and also predict the failure. Such studies are important for better utilization of the equipment. The major goal for the implementation of this integrated technique is to modify the maintenance schedule to eliminate the failure modes, and to develop the process an efficient maintenance plan to reduce the occurrence of the failures to a minimum. The constraints of this study were the availability of reliable, scientifically stored and sufficient data. It will be not correct to make a long term decision based on data for one REFERENCES 1. Rodrigo de Queiroz Souza, Alberto José Álvares,; FMEA and FTA Analysis for Application of the Reliability Centered Maintenance Methodology: Case Study on hydraulic turbines; ABCM Symposium Series in Mechatronics Vol. 3; pp E Lee, C M Evertsson; A Comparative Study between Cone Crushers and Theoretically Optimal Crushing Sequences; Mineral Engineering 2 (2011); pp Barry A. Wills, Tim Napier-Munn; Mineral Processing Technology: Elsevier Science & Technology Books; 7th Edition.. Akersten, P.A.; The Bivariate TTT-plot a Tool for the Study of Non-constant Failure Intensities; Proceedings of Society of Reliability Engineers Symposium; SRE 86 Otaniemi; Finland; 1986.

8 132 R. S. Sinha & A. K. Mukhopadhyay. Kumar Uday, Klefsjo Bengt, Granholm Seven; Reliability Investigation for a Fleet of Load Haul Dump Machines in a Swedish Mine; Reliability Engineering and System Safety; 26(1989); pp Moubray, J.; RCM-II Reliability Centered Maintenance; 2 nd Edition; New York; Industrial Press Inc; 1997