Electric motor diagnostic systems dedicated to rolling bearing fault detection impose specific signal processing requirements to ensure high effectiveness in technical condition assessment. In the case of mechanical faults, the most critical requirements include relatively long measurement durations, stable operating conditions, and high measurement accuracy. However, such conditions are difficult to achieve in real industrial drive systems, where transient states and variable operating conditions are common. Consequently, there is an increasing demand for fast and reliable fault detection methods based on the shortest possible data records.

This study investigates the influence of the data acquisition system and operating conditions on the quality of fault-related information in the rolling bearing diagnostics of induction motors. The primary objective is to determine the minimum measurement duration that enables reliable fault detection while preserving essential diagnostic features.

Experimental investigations were conducted for various types and severities of rolling bearing damage under both steady-state and transient operating conditions. To enable fully automated classification, a shallow neural network was employed. Specifically, a classifier based on Kohonen Self-Organizing Maps (SOMs) was used, with input features extracted from measurement signals using envelope spectrum analysis and selected statistical indicators.

The results demonstrate that the quality of fault-related information strongly depends on both the measurement duration and the operating conditions of the motor. Reducing the data vector length significantly degrades diagnostic performance, particularly under dynamic operating conditions. The obtained results highlight the importance of appropriate measurement duration selection in the design of fast and practically applicable diagnostic systems for induction motor bearing fault detection.