Real-Time Vibration Detection and Signal Analysis of Machines

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

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.

A Data-Driven Vibration Analysis Framework for Micro-Motor Fault Diagnosis and Quality Control

The reliability of the internal micro-motors is crucial for the performance and lifespan of electric toothbrushes. In this paper, a vibration-based fault detection method is proposed to identify micro-motor defects in electric toothbrushes. A dedicated signal acquisition device was designed and developed to capture the vibration signals of micro-motors using a high-precision accelerometer. To effectively characterize the micro-motor conditions, comprehensive features were extracted from the raw vibration data in both the time and frequency domains. A random forest (RF) algorithm was then employed to evaluate the importance of all extracted features. To better interpret the extracted features based on fault mechanisms, and to reduce dimensionality and computational overhead while avoiding overfitting, the top three features with the highest importance scores were selected to form the optimal feature subset. Finally, a support vector machine (SVM) model was utilized to classify the motor states based on the selected features. Experimental results demonstrate that the proposed method, combining RF-based feature selection and SVM classification, achieves outstanding diagnostic performance. Specifically, the model yields a balanced accuracy of 94.44%, a defect recall of 88.89%, a defect F1-score of 94.12%, a Matthews correlation coefficient of 93.74%, a geometric mean of 94.28%, and an area under the receiver operating characteristic curve of 100.00%. These robust metrics confirm that the proposed approach can accurately and efficiently detect micro-motor faults in electric toothbrushes, providing a practical and reliable solution for quality control and condition monitoring in manufacturing.

Arduino Uno-Based Vibration Monitoring System Design Using The Sensor Mpu5060

The development of manufacturing technology demands a machining process that produces high-quality and precise products. In the turning process, machine vibration is one of the factors that affect the quality of machining results, component life, and process stability. Excessive vibration can lead to a decrease in the surface quality of the workpiece as well as accelerate the wear of machine components. Therefore, a monitoring system is needed to effectively monitor the condition of engine vibration. This research aims to design and build an Arduino Uno-based lathe vibration monitoring system using MPU6050 sensors. The sensor is used to detect vibrations, while the Arduino Uno functions to process the measurement data. Tests are carried out with variations in spindle rotation speed and cutting depth to obtain vibration characteristics during the turning process. The results of the study are expected to be able to provide information on the condition of the lathe's vibration so that it can support monitoring machine conditions, preventive maintenance, and improving the quality of the turning process.

Methodology for digital processing of vibration signals in MATLAB when comparing methods of attaching an accelerometer

The article developed and tested a method of digital processing of vibration signals in the MATLAB environment, with a comparative assessment of the methods of attaching the accelerometer. Experimental studies were conducted on an asynchronous electric motor at rotational speeds of 287, 574, and 861 rpm. The vibration acceleration was recorded simultaneously on three channels, with the accelerometer attached via a eyebolt, using a magnetic base, and directly mounted on the magnetic base. The method includes checking the time scale, removing the constant component, band-pass filtering in the 2–200 Hz range, calculating time indicators, fast Fourier transformation, estimating the spectral power density using the Welch method, determining the band-pass root mean square values, performing harmonic analysis of the rotational frequency, and evaluating the coherence of the signals. It was found that at a rotational speed of 287 rpm, the root mean square values of the vibration acceleration for the three installation options diff er slightly. At 574 rpm, the root mean square values of the vibration acceleration do not differ significantly.