Olga L. Ibryaeva

Alena Antonova, Olga Ibryaeva

A new 1D search method is proposed for minimizing an arbitrary real valued function. The algorithm is a modification of the interval halving method which is based on dividing the interval of uncertainty by three points into four equal parts. The trichotomy method is based on dividing the interval by five points into six equal parts and provides the interval reducing exactly three times in every iteration. New algorithm's performance has been extensively tested and compared to well-known 1D search algorithms: the interval halving method, golden section method, Fibonacci search. The results show that the trichotomy method usually require less calculations of the values of the minimized function to determine the minimum point with the given accuracy. Also it has better accuracy when finding the minimum point after using N calculations of the function values.

V. Sinitsin, O. Ibryaeva, V. Sakovskaya et al.

Rolling bearings are one of the most widely used bearings in industrial machines. Deterioration in the condition of rolling bearings can result in the total failure of rotating machinery. AI-based methods are widely applied in the diagnosis of rolling bearings. Hybrid NN-based methods have been shown to achieve the best diagnosis results. Typically, raw data is generated from accelerometers mounted on the machine housing. However, the diagnostic utility of each signal is highly dependent on the location of the corresponding accelerometer. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a shaft-mounted wireless acceleration sensor. The experimental results show that the hybrid model is superior to the CNN and MLP models operating separately, and can deliver a high detection accuracy of 99,6% for the bearing faults compared to 98% for CNN and 81% for MLP models.