# Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings
###### Abstract  
This study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds--1002, 1500, 2400, and 3000 RPM--following four different damages--inner race, outer race, ball, and combination damage--and under two types of loading conditions. These experiments were performed on a SpectraQuest Machinery Fault Simulator(tm) by acquiring the vibration data through accelerometers under two operating conditions: with the bearing loader on the rotor shaft and without the bearing loader on the rotor shaft. The histograms showed diversity in the defected bearing as compared to the intact bearing. There was a marked increase in the kurtosis values of each damaged roller bearing. This research article proposes that histograms, along with kurtosis values, represent changes in vibration energy at damage that can easily detect a damaged bearing. This study concluded that the vibration energy at damage-based statistical technique is an outstanding approach to detect damages in roller bearings, assisting Industry 4.0 to diagnose faults automatically. fault detection; Industry 4.0; roller bearings; statistical analysis; vibration energy at damage 4.0.  
were measured by mounting the strain gauges on the gearbox and generator. Both the analyses based on the time domain and frequency domain were performed to capture components' features and find the location of the damages [6]. A novel method, called extended phase space topology (EPST), was proposed for pattern recognition and machinery diagnostics. The EPST was applied to the vibration data obtained from a rotating machine through the proximity probes. The EPST does not require any feature selection; therefore, it is easily applied in an automated process [7]. The different fault conditions in rotating machinery were identified using an efficient feature extraction technique on the raw vibrational signal so that the mechanical health status can be detected in a timely manner. The proposed feature extraction technique was based on improved multiscale dispersion entropy (IMDE) and max-relevance min-redundancy (mRMR). The proposed methodology was analyzed experimentally, and the results proved that it is a useful technique for the fault diagnosis of mechanical components, including gearboxes and rolling bearings [8]. The roller bearing diagnosis was performed at the low-energy stage of its development. The information from a machine vibration signal was extracted by amplitude level-based decomposition of the signal, and the spectral analysis was performed to extract features for bearing damage [9]. The diagnosis for a rolling element bearing was also studied using a new fault feature extraction method--decomposing the vibration signal with adaptive local iterative filtering (ALIF) and measuring the signal complexity with modified fuzzy entropy. The technique was experimentally checked, and the results concluded that the proposed technique can be used in fault diagnostics [10]. The bearing damages were also analyzed using vibrational resonance (VR) on the vibrational signals generated through simulation and experimentation. The proposed methodology was compared with the envelope spectrum, and it was concluded that the proposed VR method is better than the envelope spectrum method because the characteristic frequency was remarkably amplified in the proposed VR methodology [11]. After reviewing the approaches for the change detection and optimal segmentation of the vibrating data acquired through the operation of the rolling element bearings (REB), a new approach based on the change detection and optimal segmentation of the vibrating signal was presented, and it was concluded that the proposed technique can be used for condition monitoring and industrial processes [12]. The importance of correlated kurtosis was explored to indicate the periodicity and the impact of the signal [13].