Bearing failure in the heavy rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems acts as a preventive and corrective measure as it avoids breakdown and saves maintenance time and cost. This research paper proposes advanced strategies for early detection and analysis of taper rolling bearings. In view of this, mathematical model-based fault diagnosis and support vector machining (SVM) are proposed in this work. A mathematical model using dimension analysis by the matrix method (Dimension Analysis Method (DAMM)) and SVM is developed that can be used to predict the vibration characteristic of the rotor-bearing system. Types of defects are created using electrical discharge machining (EDM) and analyzed, and correlation is established between dependent and independent parameters. Experiments were performed to evaluate the rotor dynamic characteristic of healthy and unhealthy bearings. Experimental results are used to validate the model obtained by the DAMM and SVM. Experimental results showed that the vibration characteristic could be evaluated by using a theoretical model and SVM. Efforts have been made to extend the service life of the machines and the assembly lines and to improve their efficiency, so as to reduce bearing failure; what provides novelty to these efforts is the use of four machine learning techniques. Thus, an automatic online diagnosis of bearing faults has been made possible with the developed model based on DAMM and SVM.

Accelerometers, used as vibration pickups in machine health monitoring systems, need physical connection to the machine tool through cables, complicating physical systems. A non-contact laser based vibration sensor has been developed and used for bearing health monitoring in this article. The vibration data have been acquired under speed and load variation. Hilbert transform (HT) has been applied for denoising the vibration signal. An extraction of condition monitoring indicators from both raw and envelope signals has been made, and the dimensionality of these extracted indicators was deducted with principal component analysis (PCA). Sequential floating forward selection (SFFS) method has been implemented for ranking the selected indicators in order of significance for reduction in the input vector size and for finalizing the most optimal indicator set. Finally, the selected indicators are passed to k-nearest neighbor (kNN) and weighted kNN (WkNN) for diagnosing the bearing defects. The comparative analysis of the effectiveness of kNN and WkNN has been executed. It is evident from the experimental results that the vibration signals obtained from developed non-contact sensor compare adequately with the accelerometer data obtained under similar conditions. The performance of WkNN has been found to be slower compared to kNN. The proposed fault detection methodology compares very well with the other reported methods in the literature. The non-contact fault detection methodology has an enormous potential for automatic recognition of defects in the machine, which can provide early signals to avoid catastrophic failure and unplanned equipment shutdowns.

Owing to the frequency of occurrence and high risk associated with bearings, identification, and characterization of bearing faults in motors via nondestructive evaluation (NDE) methods have been studied extensively, among which vibration analysis has been found to be a promising technique for early diagnosis of anomalies. However, a majority of the existing techniques rely on vibration sensors attached onto or in close proximity to the motor in order to collect signals with a relatively high SNR. Due to weight and space restrictions, these techniques cannot be used in unmanned aerial vehicles (UAVs), especially during flight operations since accelerometers cannot be attached onto motors in small UAVs. Small UAVs are often subjected to vibrational disturbances caused by multiple factors such as weather turbulence, propeller imbalance, or bearing faults. Such anomalies may not only pose risks to UAV’s internal circuitry, components, or payload, they may also generate undesirable noise level particularly for UAVs expected to fly in low-altitudes or urban canyon. This paper presents a detailed discussion of challenges in in-flight detection of bearing failure in UAVs using existing approaches and offers potential solutions to detect overall vibration anomalies in small UAV operations based on IMU data.

Rolling bearings accomplishes a smoother force transmission between relative components of high production volume systems. An impending fault may cause system malfunction and its maturation lead to a catastrophic failure of the system that increases the possibility of unscheduled maintenance or an expensive shutdown. These critical states demand a robust failure diagnosis scheme for bearings. The present paper demonstrates a novel way to develop a dynamic model for the rotor-bearing system using dimensional analysis (DA) considering significant geometric, operating, and thermal parameters of the system. The vibration responses of faulty spherical roller bearings are investigated under various operating conditions for validation of the developed model. Multivariable regression analysis is performed to expose the potential of the approach in the detection of the bearing failure. Results obtained unveil the simple and reliable nature of the dynamic modeling using DA.

Bearing defects are major causes for rotary machine breakdown; hence, the dynamic behavior of bearing is crucial and important. This paper aims to present the dynamic response of bearing due to various localized defects. To study the parametric effect, three factors such as load, speed, and defect size are chosen. The Box–Behnkan method has been used to get trials to plot response surfaces. A bearing test rig has been used for experimentation with high speed, which is capable of high loading to introduce and simulate industrial application environment. Vibration and torque data have been acquired using high-precision sensors and data acquisition system. Fast Fourier transform (FFT) vibration peak and torque peak-to-peak (P2P) have been taken as the output parameter. It is observed that speed has a significant effect on both outputs and affects the bearing performance more than load. Response surfaces show that a change in load has less impact on vibration amplitude, while small variation of speed considerably increases vibration values. On the other hand, both parameters, load and speed, has a strong impact on peak-to-peak torque.

Rolling element defect identification is a difficult task. The reason being that defect on the rolling element has both rotational as well as revolutionary motion. To identify rolling element defect in a taper roller bearing, a novel signal processing scheme is proposed which results in a substantial increase in kurtosis and impulse factor of the vibration signal. The scheme constitutes a series of operations. In the beginning, the raw signal is decomposed by ensemble empirical mode decomposition (EEMD) and inverse filtering (INF). The above two stages of signal processing extract hidden impulses which are suppressed in the noise present in the experimental data. In the third stage of processing, continuous wavelet transform (CWT) using adaptive wavelet is applied to the preprocessed signal to produce a 2D map of the CWT scalogram. This transformation results in a higher coefficient in the region of impulse produced due to the defect. Finally, time marginal integration (TMI) of the CWT scalogram is carried out for defect localization. The defect frequency was evaluated with an accuracy of 97.81% and defect location was identified with an accuracy of 92%.