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Go to Editorial ManagerMonitoring the health of rotating machinery is essential to ensure system safety, achieve cost savings, and enhance overall reliability. The requirement for a reliable and clear method of identifying defects has prompted the development of several monitoring techniques. They utilize vibration, measurement of the motor's current signature, and acoustic emission data in the process of condition monitoring. The MFS (machinery fault simulator) equipment was used to determine bearing faults using vibration signal analysis. MFS conducts simulations and investigations of many bearing issues, including those occurring in the inner race, outer race, and balls. An accelerometer (type B & K 4366) was connected to a data acquisition device (IDAC-6C) to record vibration signals under different operating conditions. Furthermore, a tachometer equipped with an LCD display is employed to measure the rotational speed. Four types of defects in ball bearing (Koyo 1205C3 type) were studied, the slot in outer race with size 0.196 mm, the slot in inner race with size 0.191 mm, in ball with size 0.196 mm in additional to compound defect. In this paper, spectral correlation technique was employed to detect defects in ball bearings running at varying speed, along with spectral coherence and the corresponding Enhanced Envelope Spectrum (EES) in frequency-order domain and order-order domain. The results show that the adopted methods, that are used to analyze the real vibration signals for diagnosis the defected ball bearings, are suitable, accurate and less processing time for varying speed. The processing time of the FastACP method used to analyze the signals in order- order domain is less than that of the adopted method in the frequency-order domain for any defect type. Overall, using the FastACP method in the order-order domain significantly reduced processing time by approximately 27% compared to the adopted method in the frequency- order domain under varying speed conditions.
Vibration in rotating machines and structures is normally measured using accelerometers and other vibration sensors. For large machines and structures, the process of collecting vibration data is tedious and time-consuming due to the large number of points where vibration data must be measured. In this paper, a novel non-contact vibration measurement method has been introduced by using a high-speed camera as a vibration measurement device. This method has many advantages compared with the others. It has a low cost, easy to setup, and high automation. It also can be used for full-field measurement. Many tests have been accomplished to prove the validation of this method. The verification test has been accomplished by using the machinery faults simulator. It presented a reasonable validation that the operation deflection shapes (ODS) and the phase difference of any object can be successfully measured by using a high-speed camera. The mode shape tests have been accomplished by using the whirling of shaft apparatus device to extract the time domain, frequency domain, ODS, and phase differences for many points on the shaft at the first two critical speeds. The results proved that the high-speed camera can be used to detect the vibration signal in many different fault cases. It also proved that the high-speed camera can be used to detect the ODS and the phase angle difference. That gives the proposed method more robust and acceptance.
This paper is concerned with the design of a new controller for active suspension system. The model is considered as a quarter-car. The presented controller depends on the fuzzy technique and NARMA-L2 linearization algorithm. The compensation system that added by the fuzzy rules improves the performance of the controller, while the neural network produces the required control signal. The new controller can achieve an improvement of the ride comfort with a reasonable value of power consumption. The mathematical analysis of the mechanical power used by the model is focused on the average and the RMS of the power supplied to the system, regardless of the frequency content of the vibration signal. The simulation results which are verified by a practical examples of road profiles, demonstrate the efficacy of the proposed controller.
Diagnosing faults in rotary machines is critical, as early fault detection is a precise and essential task in minimizing operational risks and economic losses. Bearings are vital components in rotary machines and are subject to gradual degradation due to continuous operation. Failure to detect early damage can lead to problem escalation, resulting in severe damage and increased costs. In this study, two types of signals from rotary machines are analyzed: acoustic emission (AE) signals and vibration signals. These signals are utilized as input features for a deep learning neural network based on images, where the features are extracted using the Kurtogram, a powerful fourth-order spectral analysis tool that generalizes spectral kurtosis (SK) for a given signal. The results demonstrate that the accuracy of diagnosing the machine’s operational condition whether healthy or faulty ranges from 99.2% to 100%, while the accuracy of fault classification reaches 96.6%. These findings highlight the high efficiency of this methodology in fault detection and classification, establishing it as one of the most important techniques for diagnosing faults in rotary machines.
The accurate prediction of machinery faults is considered an effective strategy to increase the operation life of machines, ensure smooth operation, and provide a safe environment. Accordingly, the demands on predictive tools such as machine learning to detect machinery faults before catastrophic failure occurs has increased rapidly. In this research, a diagnosis algorithm based on using a 2D color-coded map as the input to a deep artificial neural network is proposed. These maps are called RDEgram after the processing of vibrational signals based on reverse dispersion entropy (RDE) method. The effectiveness of the proposed algorithm is investigated by testing its capability to detect different faults located at different locations on ball bearings under constant speed conditions. First, the squared envelope signal is extracted by applying the short time Fourier transform to vibration signal. Then, the RDE is used to process the squared envelope to detect the range of frequencies at which the transients occur. The RDEgram color-coded map is used to represent the RDE values as a function of frequency and frequency resolution. The maps from different fault features are collected to form the diagnostic patterns. Finally, a pretrained convolutional neural network (CNN) is applied to learn the feature pattern and diagnose the bearing faults. The CNN is trained using fixed- speed data and then it is applied to diagnose faults in the test data recorded at the same speed. The prediction method adopted in the current research shows a 100% level of accuracy for predicting two types of faults (pit and slot) located at various positions a ball bearing (KOYO 1205 C3 type) running at two constant speeds (25 and 30 Hz).