Coal mining is the process of extracting coal from the earth’s surface and also from the underground [1]. Open pit mining is a way where coal is extracted by removing a layer of soil and rock bed [2]. Different types of machinery and equipment are used in open pit mining for the removal and transport of coal. Bull dozer, dragline, bucket wheel excavator, drill rig, dumpers are some of the equipment used in open pit mining [3]. In order to meet established production targets, mining organizations require more reliable equipment [4]. Reliability specifies the overall machine condition and it can be defined as the probabilitythat a device or a system will perform its function satisfactorily for a given time periodwhen used according to stated operating conditions [5]. Around 12%-15% of manufacturing time iswasted by unplanned maintenance in open pit mining [6] and also the cost of maintenance is morethan 60% of the operation cost [7]. Therefore, the reliability of mining equipment is a major problem that needs to be addressed.
Failure Mode Effect and Criticality Analysis (FMECA) is a sophisticated technique used in industries to predict and enhance the reliability of equipment. It identifies and prioritizes the failure points of a system/sub-system before they occur. Thisconcept was originated with the US Army in 1949 to improve and inspect the reliability of military systems [8]. FMECA is proved to be one of the prominent preventive action methodologies which can avoid sudden failures. FMECA uses Risk Priority Number (RPN) which is a measure of the overall risk of a failure mode. RPN is calculated by multiplying three indices i.e., Occurrence (O), Severity (S) and Detection (D) as given in Eq. 1. These three factors are evaluated for each failure mode on a ten-point scale, usually from 10 to 1.
Risk Priority Number =\({(\text{O}}_{\text{i}\text{j}})\times {(\text{S}}_{\text{i}\text{j}})\times {(\text{D}}_{\text{i}\text{j}})\) (1)
(i = 1,2,3….n & j = 1,2,3….m)
Here, \({O}_{ij}{, S}_{ij} and {D}_{ij}\) refers to the occurrence, severity and detection of ith failure mode of jth component.
Failure modes with the greatest RPN values are thought to be more significant and should be resolved before those with lower RPN values.
One of the effective risk assessment methods that examines probable failures in order to prevent them is traditional FMECA. However, conducting classic FMECA only has a few drawbacks. The list of these negatives is shown below.
a. Duplicate RPNs
While different O, S, and D rank combinations may provide exactly the same RPN number, their hidden risk implications may vary. RPN values 270, 210, and 168, for instance, can be found in twelve different O, S, and D combinations. Similar rank and RPN values for several clusters may not necessarily reflect the same level of danger. Wasted time and resources may result from this, and in some situations, high-risk failure modes might go undiscovered.
b. Gaps in the range
The majority of values between 1 and 1000 cannot be formed, hence the RPN values produced by multiplying O, S, and D are not continuous. Only 120 out of 1,000 are RPN numbers, meaning that 88% of the range is unfilled. Therefore, RPN cannot display any integers that are multiples of 11. The multiples of 13, 17, and 19 are likewise disregarded. Following 1000, 900 is the biggest RPN value, followed by 810 and 800.
c. Lacks scientific base
The mathematical computation for obtaining RPN is debated as it lacks a complete scientific basis. There is no justification for multiplying the O, S, and D parameters to produce the RPN.
d. Uncertain information
It is challenging to assess the three risk variables O, S, and D properly. The majority of the data in FMECA is frequently ambiguous or questionable. For accurate assessment, language phrases like "medium," "many," or "very high" might be employed.
Applying fuzzy logic can be highly advantageous in addressing the limitations of conventional FMECA methods. The inherent uncertainty involved in the decision-making process may be successfully described by using fuzzy theories. This method helps to eliminate the ambiguity and uncertainty seen in conventional FMECA. Fuzzy logic was suggested by Bowles J. B. et al. [9] as a method for rating the failure scenarios in FMECA. Based on the RPNs acquired through FMECA, Feliks and Bukowski [10] devised a fuzzy logic approach to evaluate the risk in a system. The fuzzy logic-based FMECA methodology may be streamlined by Lim and Tay [11] by reducing the number of rules needed to achieve F-RPN. Chin et al. [12] suggested employing fuzzy FMEA to assess the idea of product development. Using a fuzzy logic model, Liu and Tsai [13] created preventative strategies for workplace risks in the construction sector. Albeanu G. et al. [14] developed a methodology using intuitionistic-fuzzy numbers to assess the reliability of wind turbine. Gajanand. G et al. [15] proposed a study on milling machine using fuzzy logic for reliability-centered maintenance. Renjith V. R. et al. [16] employed the Fuzzy FMECA approach to address the issues in failure mode prioritization of LNG storage facility. Gajanand G and Mishra R.P. [17] analysed the criticality of lathe machine using traditional and fuzzy approaches of FMECA. Dejan V. P. et al. [18] implemented a methodology by interlinking fuzzy sets and statistical methods to analyze the risk level of a system.
The present study extends the application of fuzzy logic to FMECA, specifically focusing on prioritizing failure modes. Fuzzy FMECA offers a superior approach that can produce reliable results, even when faced with limited information, in contrast to traditional FMECA.