The side drum coal washing machine can separate coal and gangue, improve utilization, reduce losses and transportation costs. With the development of technology, the failure rate is getting lower and lower, but it is also impossible to avoid the occurrence of faults. Therefore, we need to master the basic diagnostic thinking and confirm where the problem is.

　　Firstly, it is necessary to determine the content of the operation status detection of the coal washing equipment, establish a monitoring system for testing, analysis, and information extraction, and monitor, judge, and predict the status.

　　The formal logic reasoning of coal preparation equipment involves observing, hypothesizing, verifying, correcting hypotheses, and revalidating until the true cause of the fault is identified. In detail, the process of diagnosing coal washing machine faults can be divided into five steps: conducting research and investigation to determine the fault phenomenon, assuming the cause of the coal preparation machine fault, and then inferring the expected result from the assumed cause. The inferred result is compared with the observed phenomenon, Finally, a conclusion can be drawn on whether the hypothesis of the side drum coal washing machine is valid or not. If the original hypothesis is not valid, a new hypothesis needs to be established and so on.

　　The fault tree analysis method first selects a system fault with a significant impact on the coal washing equipment as the top event, and then decomposes the selected cause of the system fault into intermediate events step by step until the basic event that cannot or does not need decomposition is taken as the bottom event. This results in a tree like logic diagram called the fault tree. Generally speaking, the fault tree analysis of coal washing machinery is based on the fault tree, Analyze the types of bottom events that affect the occurrence of the top event, the side drum coal washing machine and its relative impact degree, in short, establish a fault tree, conduct qualitative and quantitative analysis of the fault tree.