Multi-Sensor Condition-Monitoring Dataset of a Brushed DC Servo Motor

Description

Dataset Short Description This dataset contains raw multi-sensor recordings from a brushed PM DC servo motor (3PI12.12) operated under multiple conditions and load levels. It includes four sensor modalities: armature current waveforms (BIN) vibrometer waveform audio (WAV) smartphone audio (M4A) vibrometer spot measurements (XLS) The dataset is organized into four main condition families: normal operation, loose foundation, suboptimal speed-regulator tuning, and suboptimal speed-regulator tuning with RT (current-regulator) coefficient variation. Each family is provided in two variants: without reversal (constant rotation direction) and with reversal (rotation direction reversed every 4 seconds). The files are organized by condition and sensor, with metadata in metadata.csv, and are intended for condition monitoring research such as fault classification, load estimation, and phone-vs-instrument benchmarking.

Steps to reproduce

The 3PI12.12 motor (see Section 2) was driven by a 4-quadrant thyristor (SCR) converter with armature voltage/current control. Mechanical load was applied with a coupled load unit and set to 1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 % of rated power. Conditions. The full load sweep was repeated for each condition in Section 3 (normal without reversal, normal with periodic direction reversal, and a deliberately loosened foundation without reversal). Acquisition (each sensor recorded separately, under the same operating conditions — not simultaneously): Armature current — Rigol MSO5074 oscilloscope, saved as native binary waveforms (.bin); sample rate and scaling are in each file header (~20 s per capture). Vibration waveform — AV-160B vibrometer external piezoelectric probe; the 2.0 V AC analog output recorded as 44.1 kHz / 16-bit stereo WAV (~20.5 s). Vibration spot readings — the same AV-160B in display mode (velocity, acceleration, displacement per ISO 2954), logged to XLS. Acoustic — a budget Android smartphone microphone ~1 m from the machine, saved as M4A (AAC). Procedure. For each condition and load level the motor was brought to steady state, then each sensor was recorded in turn. Files are named load{NNN}_{sensor} and organised under data/<condition>/<sensor>/; see metadata.csv for the full inventory with sample rates and durations. More details added in the Readme.md in the dataset.

Institutions

• Technical University of Sofia

  Sofia-Capital, Sofia

Categories

Licence