Experimental vibration time-series data for rotor crack detection and condition monitoring

Vibration Dataset for Crack Analysis and Detection in a Rotating Bladed System

This work presents conditioned and normalized vibration signal datasets acquired from the spanwise axis of the three blades of a rotating bladed system operating at a constant rotational speed of 240 rpm. The conditioned dataset was obtained using piezoelectric accelerometers mounted at the blade roots. The accelerometer output signals were conditioned and recorded by a dedicated data acquisition system. The signals were acquired under both healthy and damaged operating conditions. Baseline vibration signals were first recorded with all three blades in a healthy condition. Subsequently, cracks were deliberately introduced at three different locations along the blade span—the root, middle, and tip zones. Each crack location was independently evaluated on each of the three blades, resulting in a comprehensive dataset that includes healthy operation and all combinations of blade–damage locations. The datasets enable analysis of the system’s vibratory response and of dynamic information propagation toward the blade root, depending on the crack zone. Their main contribution is to provide reliable experimental data for the development, validation, and benchmarking of vibration-based diagnostic and structural health monitoring techniques. Furthermore, the datasets serve as valuable resources for advancing early crack detection strategies and enhancing the reliability of rotating industrial equipment with blades, such as fans, compressors, turbines, and aerogenerators.

Machine learning-based fault detection in low-speed bearings using a multi-environmental dataset

The near-perfect linear separability indicates that the dataset’s binary, controlled-laboratory labelling rather than intrinsic bearing-degradation physics drives the clean classification, and validation on 500–1000 or more samples with progressive-degradation labelling is essential before any operational claim can be supported.

Fault Diagnosis of Automotive Gasoline Engines Using Time–Frequency Vibration Image Analysis

Artificial Neural Network-Based Diagnosis of Wind Turbine Blade Faults Using Vibration Analysis at Constant Operational Speed

Wind turbine blade faults, such as surface erosion, cracks, mass imbalance, and twist deformation, significantly compromise operational efficiency and reliability, thereby increasing maintenance costs. This research presents an artificial neural network (ANN)-based diagnostic approach for identifying five distinct fault states in wind turbine blades using vibration signal data collected at a constant operational speed of 1.3 m/s. The dataset, which encapsulates real-world vibration responses under varying fault conditions, was analyzed to extract amplitude features for classification. A balanced dataset of 500 samples per class was used to ensure robust training and evaluation. The ANN model achieved highly reliable performance, with classification accuracies (CA) of 96.21% (crack), 97.12% (erosion), 95.47% (healthy), 96.04% (twist deformation), and 94.38% (mass imbalance). Corresponding F1-scores were 95.32%, 96.51%, 94.45%, 95.18%, and 93.36%, respectively. These results confirm the model's effectiveness in distinguishing between common wind turbine blade faults and healthy conditions. This study demonstrates the potential of ANN-based systems for intelligent fault detection in wind energy systems, aiding in the advancement of condition-based maintenance and operational safety.

Vibration Analysis of High-Speed Rotating Machinery

The saving grace of industrial systems in the present day is high-speed rotating machinery which encompasses turbines, compressors, generators and aerospace propulsion units. The successful performance of such machines largely remains the responsibility of efficient condition monitoring and fault diagnosis methods. Vibration analysis has become one of the most potent and popular in the number of these techniques. A cohesive exploration of the vibration nature of high-speed rotating machinery with its focus on signal acquisition, signal processing, feature extraction, and fault classification techniques is discussed in this paper. The process combines both experimental measurements, mathematical modeling and using advanced signal processing to detect typical mechanical faults including imbalance, misalignment, bearing flaws, shaft cracks and gear mesh anomaly. An elaborate experimental design is crafted based on an accelerometer, data collection apparatus, and spectral analysis apparatus to record the signature of vibrations at varying operation conditions. The time-domain analysis, frequency-domain abasys and time-frequency-domain analysis are used to extract diagnostic features that are usually significant. Short-Time Fourier Transform (STFT), Fast Fourier Transform (FFT), and Wavelet Transform (WT) techniques are adopted to make a fault more detectable. In addition, automated fault recognition is performed with the help of statistical indicators and classifiers based on machine learning. The findings indicate that vibration-based diagnostics have demonstrated high relative accuracy of early fault detection and reliability of the system. Comparative study shows that the hybrid signal processing solutions are better than the conventional methods in complicated operational scenarios. The given methodology has offered a systematic framework of being predictive in maintenance developed in industrial rotating machines. The results of this study help in making the operations safe, minimizing downtime and minimizing costs of maintenance. The research can be used by the researchers and practitioners who wish to adopt modern vibration monitoring systems in the rotating machines that operate at high speed.

Experimental investigation of vibration characteristics in a small-scale rotating system using time- and frequency-domain analysis

This research stems from the problem that adding unbalance mass to a rotating shaft alters system vibration characteristics, a phenomenon that remains insufficiently quantified in small-scale rotating engines. This study aims to analyze the effects of variations in mass position, radial distance, and rotational speed on the vibration characteristics of a small-scale engine using combined time-domain and frequency-domain approaches. A quantitative experimental design was conducted across 27 treatment combinations, evaluating mass distances (5-25 cm) and speeds up to 860 rpm (14.33 Hz). Data acquisition utilized an accelerometer-microcontroller setup, analyzed via peak acceleration, RMS, and FFT methods. Results show a direct proportional relationship between mass radial distance and vibration amplitude, with the highest response observed at a 25 cm load distance and 860 rpm. The y1-axis exhibited the highest acceleration and RMS values, identifying it as the most sensitive measurement axis for condition monitoring. FFT analysis revealed dominant spectral peaks at the fundamental shaft rotational frequency (approximately 14.3 Hz at 860 rpm), accompanied by sub-synchronous and harmonic components induced by mass imbalance. In conclusion, vibration response in small-scale engines is heavily governed by mass location and rotational speed, underscoring the necessity of strategic sensor orientation for accurate fault detection.