Remaining life prediction method and terminal of equipment cluster based on single-unit life evolution
A life prediction and equipment technology, applied in prediction, data processing application, calculation, etc., can solve problems such as cost increase, low efficiency of artificial life evolution, uncontrollable future life and future state of a single (set) equipment, and achieve improvement The effect of work efficiency
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Embodiment 1
[0052] A method for predicting the remaining life of equipment clusters based on the life evolution of single equipment, see figure 1 , including the following steps:
[0053] S1: Receive input data; the input data includes equipment life data and model parameters input by the user; the model parameters include gear parameters;
[0054] The model parameters include replenishment schedule, decommissioning schedule, equipment type consumption rate parameters, and the like. The decommissioning schedule contains the specific actions that the user wants to perform on which equipment in the future. The equipment type consumption rate parameter is a control parameter that can directly affect the prediction result of equipment consumption in the service life.
[0055] For example, a replenishment schedule includes data such as equipment type, total prescribed hours, prescribed lifespan, and year. The decommissioning schedule includes data such as equipment type, equipment serial nu...
Embodiment 2
[0071] Embodiment two adds the following content on the basis of embodiment one:
[0072] After the method combines the initialized gear parameters to obtain the prediction scheme, it also includes:
[0073] Set optimization strategy;
[0074] The execution distribution of the prediction scheme is optimized according to the optimization strategy, so as to obtain the best execution strategy with the fastest speed when executing the prediction scheme.
[0075] Specifically, the execution of the prediction plan is driven by a multi-threaded architecture, so how many threads are needed for the execution of the prediction plan, and how many sets of plans are required for each thread, can make the execution of the prediction plan the fastest. At this time, the prediction plan is allocated according to the optimization strategy Optimize to achieve the best execution strategy.
[0076] Preferably, the verifying and correcting the initialized input data specifically includes:
[007...
Embodiment 3
[0087] see figure 2 , the prediction of the remaining service life hours of the equipment according to the prediction scheme and the verification data specifically includes:
[0088] S21: If there is currently a repair and delivery schedule, read the life hours of the repaired equipment in the repair and delivery schedule, add the remaining life hours in the verification data to the life hours of the repaired equipment to obtain the equipment new remaining life hours;
[0089] S22: If there is no repair delivery schedule currently, calculate the delivery date of the equipment according to the increased maintenance amount parameter in the forecast scheme and the preset equipment repair period.
[0090] Specifically, the input of steps S21-S22 is the equipment life data of M months, the repair and delivery schedule and the additional repair amount parameter in the forecast scheme. The output is the new remaining life hours of the equipment. First, compared with the repaired ...
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