Method and system for analysing an equipment for current cycling test

A system for analysing an equipment for current cycling test, comprising:

• a processor;
• a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to:
• receive an input dataset corresponding to the equipment,
• wherein the input dataset comprises a set of test control parameters, specification data, historical cycling test data and a set of test setup parameters;
• analyse the equipment based on the input dataset using an artificial intelligence (AI) model,
• wherein the analysis is based on a validation of the specification data, the set of test control parameters and the set of test setup parameters with respect to the historical cycling test data, and
• wherein the AI model is trained to validate the specification data, the set of test control parameters and the set of test setup parameters based on the historical cycling test data;
• predict an outcome of the current cycling test based on the analysis as one of failure or pass;
• and
• upon prediction of the outcome as failure, determine a reason of failure by prompting a generative AI model, wherein the generative AI model is prompted based on the specification data, the historical cycling test data and the set of test setup parameters.

A non-transitory computer-readable medium storing computer-executable instructions for analysing an equipment for current cycling test, the computer-executable instructions configured for:

• receiving an input dataset corresponding to the equipment,
• wherein the input dataset comprises a set of test control parameters, specification data, historical cycling test data and a set of test setup parameters;
• analysing the equipment based on the input dataset using an artificial intelligence (AI) model,
• wherein the analysis is based on a validation of the specification data, the set of test control parameters and the set of test setup parameters with respect to the historical cycling test data, and
• wherein the AI model is trained to validate the specification data, the set of test control parameters and the set of test setup parameters based on the historical test data;
• predicting an outcome of the current cycling test based on the analysis as one of failure or pass;
• and
• upon predicting the outcome as failure, determining a reason of failure by prompting a generative AI model, wherein the generative AI model is prompted based on the specification data, the historical cycling test data and the set of test setup parameters.