Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the efficiency of some primary discriminant func- tions applied in planetary gearbox diagnostics. Real data for planetary gearboxes mounted in bucket wheel excavators working in field condition are elaborated. The aim is to perform condition monitoring (faulty or healthy) of such devices. The raw recorded data (vibration series emit- ted by the device) re first segmented and transformed to frequency domain using por spectra densities (PSD). Next, 15 variables denot- ing amplitudes of derived spectra re extracted. This yielded two data matrices A and B of size 1232× 15, and 951× 15, representing the faulty and the healthy device appropriately. The data are non-Gaussian and the covariances in both groups differ significantly. Now, using Fisher's discriminant criterion and the kernel method- ology,  construct from a learning sample (counting only 600 items) a discriminant function able to provide a discriminant score for distin- guishing beten the healthy and faulty state of a gearbox. The function proved to be very effective: Both for the learning and the testing data samples (600 and 1483 data vectors respectively)  got 100% correct as- signments to the 'faulty' and 'healthy' class, with a conspicuous margin beten the two classes. The results are visualized in a 2D plane.
['Anna Bartkowiak', 'Radoslaw Zimroz']
I am writing a research paper on a idea Efficacy of some primary discriminant functions in diagnosing planetary gearboxes
[{"from": "human", "value": "I am writing a research paper on a idea Efficacy of some primary discriminant functions in diagnosing planetary gearboxes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the efficiency of some primary discriminant func- tions applied in planetary gearbox diagnostics. Real data for planetary gearboxes mounted in bucket wheel excavators working in field condition are elaborated. The aim is to perform condition monitoring (faulty or healthy) of such devices. The raw recorded data (vibration series emit- ted by the device) re first segmented and transformed to frequency domain using por spectra densities (PSD). Next, 15 variables denot- ing amplitudes of derived spectra re extracted. This yielded two data matrices A and B of size 1232\u00d7 15, and 951\u00d7 15, representing the faulty and the healthy device appropriately. The data are non-Gaussian and the covariances in both groups differ significantly. Now, using Fisher's discriminant criterion and the kernel method- ology,  construct from a learning sample (counting only 600 items) a discriminant function able to provide a discriminant score for distin- guishing beten the healthy and faulty state of a gearbox. The function proved to be very effective: Both for the learning and the testing data samples (600 and 1483 data vectors respectively)  got 100% correct as- signments to the 'faulty' and 'healthy' class, with a conspicuous margin beten the two classes. The results are visualized in a 2D plane."}]