Order Tracking Methods for Fault Diagnosis in Rotating Machinery

Order tracking techniques form a cornerstone of non-stationary vibration analysis in rotating machinery, enabling fault diagnosis under variable-speed conditions. By transforming time-domain signals into the order domain through angular resampling, these methods reveal characteristic harmonics related to shaft rotation. Computed order tracking (COT) remains a widely adopted approach, employing interpolation of tachometer pulses and synchronous sampling. Time–frequency representations, such as short-time Fourier and wavelet transforms, enhance the extraction of instantaneous frequency ridges for adaptive resampling. In parallel, tacholess order tracking methods have emerged, deriving angular information directly from vibration data via energy operators or singular-value decomposition, thereby eliminating the need for dedicated speed sensors. Combined with envelope analysis, cyclic spectral correlation and demodulation transforms, order tracking enables precise isolation of bearing, gear and rotor anomalies. Recent advances integrate machine learning and deep neural networks with order-domain features, improving automatic fault classification across varying operational regimes. These developments have global significance for industries spanning power generation, transportation and manufacturing, where early detection of bearing wear, gear tooth damage and imbalance can prevent catastrophic failures, reduce downtime and enhance maintenance strategies.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Order Tracking Methods for Fault Diagnosis in Rotating Machinery publication trend

The graph below shows the total number of articles in order tracking methods for fault diagnosis in rotating machinery across all publications each year (not limited to Nature Index journals).

Technical terms

Order tracking: Conversion of vibration data to the order domain by resampling against rotational angle, revealing speed-proportional harmonics.

Tacholess order tracking: Order tracking without external speed sensors, estimating instantaneous angular speed directly from vibration signals.

Angular resampling: Process of interpolating time-domain data at uniform shaft-angle increments to create an order-domain signal.

Time–frequency representation: Joint analysis framework (e.g. short-time Fourier or wavelet) that displays signal frequency content over time.

Envelope analysis: Technique extracting amplitude modulation of high-frequency resonances, often used to detect bearing faults.

Synchronous sampling: Acquisition of vibration signals in precise relation to angular position, ensuring alignment of orders.

References

1. Extraction of instantaneous frequencies from ridges in time–frequency representations of signals. Signal Processing (2016).
2. Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review. IEEE Sensors Journal (2023).
3. Accurate Assessment of Computed Order Tracking. Shock and Vibration (2005).
4. A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds. Sensors (2021).
5. Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences. Chinese Journal of Mechanical Engineering (2019).
6. Use of the Teager Kaiser Energy Operator to estimate machine speed. PHM Society European Conference (2016).
7. A Tacholess Order Tracking Method Based on Inverse Short Time Fourier Transform and Singular Value Decomposition for Bearing Fault Diagnosis. Sensors (2020).