Vibration Analysis of High-Speed Rotating Machinery

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

Experimental vibration analysis and baseline signature establishment for a high-speed vapour blower gearbox assembly

Vibration analysis plays a crucial role in ensuring the reliability, safety and efficient operation of rotating machinery by enabling early fault detection and condition monitoring. In the present study, vibration characteristics of a high-speed vapor blower-gearbox assembly operating at speed from 1,000 to 16,000 rpm were investigated. Frequency-domain analysis and impact testing were performed using multi-axial acceleration data collected from key bearing locations at both the drive-end and non-drive-end to identify the primary excitation mechanisms. Experimental data reveal that structural resonance, rather than rotational imbalance, predominantly influences the peak vibration amplitudes. The critical natural frequencies, which are linked to the casing support, low-speed shaft coupling and base structure, have been identified in the range of 12,000 to 15,000 rpm. To address the issue of resonance amplification, ribs were strategically integrated into the structure, resulting in an additional mass of 2.5-3.0 kg to enhance stiffness. This modification effectively shifted the natural frequencies of the system away from the operational velocity range, yielding a 30–40% reduction in vibration levels. These findings demonstrate that stiffness optimization enhances dynamic stability, providing a robust foundation for condition monitoring and prognostics of high-speed turbo machinery.

Evaluation of Vibration Characteristics Signatures for Condition Monitoring of Rotor Bearing System by Using Box-Behnken Experimental Design

To address the complexity of vibration in ball bearings with composite defects during actual operation, bearings play an imperative role in ensuring the smooth, low friction operation of rotating machinery by reducing friction. Bearings operating within the low to medium speed range are commonly used in a wide variety of industrial, automotive, and machinery applications. This paper proposes a dynamic model for the complex, combined effects of load, localized defects, and misalignment on the vibration characteristics of rotor-bearing systems. To establish a robust theoretical framework, dimensional analysis using the matrix method was first employed to identify the principal dimensionless groups governing the system's dynamic behaviour. Finally, the model proposed is verified by experiments, and the influence of misalignment, speeds, defect sizes, and loads on the vibration characteristics of the bearing is investigated. Response Surface Methodology (RSM), specifically a Box-Behnken Design (BBD), was implemented to systematically plan the experiments and quantify the interaction effects between the input variables. Vibration amplitudes were recorded under various combinations of applied load, defect severity, and misalignment levels. The BBD analysis provides a comprehensive model of the system's response, confirming that increases in both load and misalignment significantly elevate vibration amplitudes. The work of this paper can improve the accuracy of rotor compound fault identification and provide references for vibration control of rotating machines, fault diagnosis, and life prediction.

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.

Incipient Fault Diagnosis of Cylindrical Roller Bearings Using Dynamic Response Analysis

Detecting faults early on is important in order to maintain the health of rotating machinery. Incipient faults in rolling-element bearings lead to the generation of micro-defects which create weak transient impact signals. However, these are often buried within the background vibration of the machine and structural transmission effects. Indicators based on traditional time-domain methods possess sensitivity to impulsive damage, but when the signal exhibits non-stationarity or a noisy trend. The current study compares time-domain statistical parameters and FFT spectral analysis for progressive inner race damage in an NJ307 cylindrical roller bearing. A wire electrical-discharge-machining technique produces artificial inner-race defects with sizes of 0.25 mm, 0.50 mm, 0.75 mm and 1.00 mm, while a healthy bearing serves as the reference condition. Vibration is measured with a single-axis accelerometer connected to an eight-channel Dewesoft data- acquisition system with a sampling frequency of 20 kHz; the shaft speed is 800 rpm by use of a variable-frequency drive. The RMS, standard deviation, peak value, crest factor, kurtosis, and form factor are taken into account during the analysis. FFT spectra are observed at the calculated inner-race fault frequency and its harmonics. The findings reveal that at the earliest damage stage, there is only a gradual change in both RMS and standard deviation. In contrast, changes in peak value, crest factor, kurtosis, and amplitudes of the BPFI-related FFT are more robust indicators of progressive damage. The FFT provides statistically interpretable frequency information, which scalar time-domain features cannot. As suggested by the findings, the proposition is for a combined diagnostic procedure in which the time-domain features provide a rapid screening while the FFT confirms the bearing-fault frequency. Keywords: cylindrical roller bearing; incipient fault; dynamic response; condition monitoring

Incipient fault diagnosis of rolling element bearings using vibration, sound and tribological analysis: A literature review

Rolling element bearings are used in rotating machines in aviation, chemical, and nuclear industries. A failure to detect faults in the rolling bearing causes unexpected breakdown of rotating machines. Detecting bearing defects early on is still a challenge since micro- faults have less energy. Early-stage defects from fatigue, misalignment, overload and poor lubrication create low-energy signals that can be masked. This article summarises some key measurement methods, i.e. vibration and acoustic signal measurement, tribological parameter analysis, wear debris analysis, and thermal measurement, in rolling element bearings. All the methods will be evaluated with respect to a sensing mechanism, signal processing algorithm, micro-defect sensitivity, and operational constraints of the anti-friction bearing. To deal with the drawbacks of single sensors, we demonstrate the use of recent advancements in multi-sensor data fusion and machine learning models through examples where kinetic, acoustic and chemical metrics are fused for improved classification outcomes. In summary, multi-sensor data fusion greatly improves the accuracy of early fault detection, and it can greatly enhance predictive maintenance systems. Keywords: Vibration, Acoustic, Wear Debris, Tribology, Condition Monitoring, Bearing