New Open Dataset Captures Rotor Blade Faults Under Ever-Changing Speeds

Rotating machines rarely live quiet lives at a single, steady speed. Aero-engines spool up and throttle back, wind turbines ride gusts that never stop changing, and gas and steam turbines ramp through transient regimes on their way to and from full power. Yet the vast majority of public datasets used to train and validate fault-diagnosis algorithms were recorded on test rigs running at constant speed, a simplification that quietly divorces the data from the messy reality of industrial operation. A team at the Laboratory of Fault Diagnosis and Prognostics at Anhui Agricultural University in Hefei, China, has now released a labelled vibration dataset designed to close that gap, capturing rotor blade damage under both stationary and continuously time-varying speed profiles.

The dataset, described in the journal Results in Engineering by Qing Li and Hao Xu, documents experiments on a rotor-bearing-gearbox system in which five distinct blade conditions were tested: a healthy blade and four seeded faults, namely a groove, wear, a partially broken section and a bent blade. Each condition was recorded under a stable speed of 20 Hz and under four non-stationary scenarios: continuously increasing speed, continuously decreasing speed, increasing then decreasing speed, and decreasing then increasing speed. The result is a matrix of 25 datasets covering every combination of health state and speed trajectory, all synchronously sampled at 20 kHz together with the instantaneous rotational speed signal.

The motivation is straightforward but consequential. Incipient blade faults such as small cracks, grooves or wear patches change the local mass distribution and stiffness of a rotor, disrupting its circumferential symmetry. Under steady conditions these faults produce characteristic, well-understood signatures. But when the shaft speed is sweeping continuously, the fault frequencies smear across the spectrum, a phenomenon known as spectral smearing, and the diagnostic features that work beautifully at constant speed can dissolve into noise. Machines in the field almost always operate in these non-stationary regimes, which means algorithms validated only on constant-speed data may fail precisely when they are needed most, during start-up, shutdown and load transients, when the risk of resonance-driven failure is elevated.

The experimental platform itself is a carefully instrumented drivetrain. A three-phase asynchronous motor, governed by a variable frequency drive, turns a shaft through a coupling into double-support bearings of type PH205, carrying a 120 mm aluminium alloy rotor disc fitted with six blades. Thirty-six M5 threaded holes, spaced at 10-degree intervals around the disc, allow precise dynamic balance adjustment. Downstream, a reducer gearbox connects to a magnetic powder brake, whose adjustable input current lets the researchers impose a controlled resistive load anywhere from 0 to 6 Nm. For all the runs in this dataset the brake was held at a fixed 4 Nm, isolating speed variation as the changing experimental factor.

The four fault types are quantitatively defined rather than vaguely described, which matters enormously for reproducibility. The groove fault is a cut 3 mm wide and 35 mm long; the wear fault removes material to a depth of 1.5 mm; the broken fault removes a 35 mm region of the blade; and the bending fault deforms the blade by 10 degrees. The healthy reference blade is 2 mm thick, 60 mm long and set at a 30-degree blade angle. Because each fault is a precisely specified geometric modification, other laboratories can in principle reproduce identical damage on their own rigs and compare results directly, something rarely possible with naturally occurring faults.

Each speed scenario was executed with care. In the stable-speed runs, 60 seconds of data were collected at 20 Hz and split into two 30-second parts. The ramping runs varied in duration, from roughly 15 seconds for a simple deceleration to nearly 39 seconds for a full up-down sweep, with speeds ranging between about 9 Hz and 20.75 Hz depending on the trajectory. Multiple independent repeated trials were conducted for each health condition and speed profile, with the fault condition, sensor arrangement, load and prescribed speed curve held identical across repetitions. This repetition strategy reduces the influence of random experimental disturbances and measurement variability, and gives users a basis for assessing the consistency of vibration responses under nominally identical conditions.

The sensing arrangement captures three-axis acceleration, with channels in the X, Y and Z directions recorded from an accelerometer mounted on the right side of the rotor disc, alongside the input velocity signal. Each data file is organised into five columns: operating time, acceleration in X, acceleration in Y, acceleration in Z, and rotational speed. A systematic file-naming convention encodes the health condition, the speed scenario and the dataset number, so that a file labelled 2_3_Groove_Up_Down, for instance, unambiguously identifies a grooved blade run through an increasing-then-decreasing speed profile. This kind of disciplined organisation is unglamorous, but it is exactly what turns a pile of recordings into a usable benchmark.

The authors also demonstrate one analytical workflow that the dataset supports: envelope order spectrum analysis, a technique built to defeat spectral smearing under variable speed. The method band-pass filters the vibration signal, applies a Hilbert transform to extract the amplitude envelope of the modulated carrier, resamples the envelope uniformly in the angular domain using the integrated instantaneous rotational frequency, and then applies a discrete Fourier transform to produce a spectrum expressed in orders of the shaft rotation rather than in hertz. Because the angular domain is tied to shaft position rather than wall-clock time, features that would blur in a conventional frequency spectrum remain sharp regardless of how fast the shaft is turning.

Applied to the increasing-speed run of the grooved blade, the technique is revealing. The band-pass filtered signal shows periodic fluctuations confirming continuous pulse excitation from the damaged blade, and the overall envelope amplitude grows with time as the rising speed injects more impact energy. In the resulting envelope order spectrum, distinct peaks appear at 1.00X, 2.01X, 4.02X and 7.03X, matching the theoretical vibration features of a grooved blade, while noise components stay at a low level. The physics is intuitive: the local material removal disrupts the circumferential symmetry of the rotor, producing a periodic excitation once per revolution that yields the dominant 1X component, while the non-sinusoidal character of the excitation, filtered through the structural transfer path and nonlinear response of the system, generates the higher-order harmonics.

The research landscape that motivated this release is telling. The celebrated Case Western Reserve University bearing dataset, the Paderborn University bearing collections, the IEEE PHM 2012 run-to-failure bearing data, the PHM 2009 gearbox challenge data and the Tsinghua University gearbox datasets have collectively underpinned two decades of progress in data-driven diagnostics, yet nearly all were recorded at constant speed or at discrete speed steps, and almost none address rotor-blade damage specifically. By publishing labelled blade-fault data under genuinely continuous speed variation, the AAU team opens the door to benchmarking speed-conditioned feature extraction, cross-condition fault diagnosis, self-supervised learning and uncertainty-aware diagnostic methods on problems that actually resemble field conditions. The authors are candid about limitations: only five health states are covered, laboratory safety precludes the 3000 to 5000 rpm range of real turbomachinery, and the brake load is fixed. Future extensions, they note, will broaden the damage modes, widen the speed range, add adjustable loading, nonlinear ramps and random speed fluctuations. Even so, for a field long starved of non-stationary rotor data, this release is a welcome and overdue gift.

Subject of Research: A public rotor-blade vibration dataset for fault diagnosis under time-varying rotational speed conditions

Article Title: AAU Rotor Vibration Dataset Under Time-Varying Speed Scenarios

Article References: Li, Q., & Xu, H. (2026). AAU Rotor Vibration Dataset Under Time-Varying Speed Scenarios. Results in Engineering, 32, Article 113172. https://doi.org/10.1016/j.rineng.2026.113172

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113172

Keywords: rotor blades, vibration monitoring, fault diagnosis, time-varying speed, open dataset, envelope order spectrum, predictive maintenance, rotating machinery, machine learning benchmark, signal processing, Rotor, Vibration