Root Cause Analysis (RCA) refers to an approach that involves identifying the causes behind system failures. In doing so, it is concerned with answering the question: “What led to the problem?” High-tonnage conveyor belts often suffer from repeated faults, which lead to major obstacles, safety concerns, and financial implications for the company. It is important to carry out an RCA to ensure that the real cause is established, thus enabling a long-term solution to the problem.
The first step in the process entails assembling a diverse group of professionals including engineers, operators, and maintenance staff to collect complete information about operations (Bogdanovská et al., 2025). This group needs to document all past maintenance records, sensor data, and downtimes to formulate a clear picture of the situation. It is crucial to have an accurate picture of the actual loading scenario, environmental conditions, and history of failures.
Additionally, the fault is determined by means of systematic analysis methods such as the Failure Mode and Effects Analysis (FMEA), which examines the possible modes of failure in a particular component like belt joints, pulleys, or idler rollers and considers the severity, probability of occurrence, and probability of detection of each component (Bogdanovská et al., 2025).
The failure modes having been identified, a technique such as the “5 Whys” or the Fishbone Diagram will be used by investigators to track back to the root cause of the problem. Modern-day methods of fault detection within industries often involve computerized fault diagnostic systems that will not only identify the problem but also find its root cause through parameters such as belt tension and motor vibrations (Alfarizi et al., 2023).
As soon as the root cause is known, specific countermeasures have to be planned and implemented. This could involve strengthening the belt against impact loads by changing its material, optimizing the loading chute system to ensure efficient material movement, or even better management through the use of predictive maintenance (Bogdanovská et al., 2025). All proposed measures need to be assessed thoroughly for their feasibility and economic viability before being applied.
In summary, the RCA process ends with constant monitoring and evaluation of the system. Once corrections are done to prevent any future recurrence of the problem, it will be important for the operator to monitor the system’s operation constantly to ensure that all the problems have been addressed successfully. The process ensures proactive instead of reactive maintenance of the system.
References
Alfarizi, M. G., Vatn, J., & Yin, S. (2023). An extreme gradient boosting aided fault diagnosis approach: A case study of fuse test bench. IEEE Transactions on Artificial Intelligence, 4, 661–668. https://doi.org/10.1109/tai.2022.3165137
Bogdanovská, G., Benková, M., & Bednárová, D. (2025). Analysis of causes and consequences of failures in process of andesite crushing by jaw crusher. Processes, 13(1), 225. https://doi.org/10.3390/pr13010225
Papageorgiou, K., Theodosiou, T., Rapti, A., Papageorgiou, E. I., Dimitriou, N., Tzovaras, D., & Margetis, G. (2022). A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing. Frontiers in Manufacturing Technology, 2. https://doi.org/10.3389/fmtec.2022.972712
