In‐Process Quality Inspection of Rolling Element Bearings Based on the Measurement of Microelastic Deformation of Outer Ring

Abstract

Quality inspection is the necessary procedure before bearings leaving manufacturing factories. A testing machine with low shaft speed and light radial load condition is generally used to test the dynamic quality of bearings, which avoids creating any potential damages to testing bearings. However, the signal of defective bearings is easily polluted by very weak noise using the traditional vibration-based measurement method due to the low shaft speed and light radial load condition specified for nondestructive inspection, which needs complicated and time-consuming calculation and is not suitable for online inspection. Thus, there are problems about special operating conditions and weak fault severity in quality inspection of bearings, which is quite different from the fault diagnosis of bearings. In this paper, a novel dynamic quality evaluation technique is proposed based on the measurement of Hertz deformations. The measurement system is mainly composed of an eddy current sensor, sensor fixture, and data acquisition platform with less transfer path than the vibration-based measurement system. The sensor fixture is optimized through numerical simulations to obtain signals with a high signal-to-noise ratio. Accurate evaluation of dynamic quality can be implemented reliably with simple signal processing. The proposed method can be used with a rotating speed of 100 rev/min and test load of 100 N, which is remarkably lower than the traditional quality inspection machineries with a rotating speed of around 1000 rev/min and the test load of 400 N. Both simulation and experiment studies have verified the proposed method.

Health Degradation Monitoring of Rolling Element Bearing by Growing Self- Organizing Mapping and Clustered Support Vector Machine

“…Each BLIMF is concentrated around a center pulsation ω k fixed during the decomposition process. And the bandwidth of each BLIMF in spectral domain is estimated using squared L 2 − norm of the gradient. Therefore, the signal decomposition in the VMD algorithm is treated as solving a constrained variational problem, which is given as:…”

“…Rolling element bearing (REB) is one of the most widely used parts in mechanical equipment, which is a precise mechanical element to change the sliding friction between the running shaft and the shaft seat into rolling friction so as to reduce friction loss [1], [2]. However, the REB is a vulnerable part of mechanical equipment.…”

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

“…The data types of data-driven method can be divided into single signal and multi-signal fusion. Single signal, such as vibration signal [3,18,[28][29][30], acoustic signal [31][32][33][34], current signal [24], [25], temperature signal [21], pressure signal [35], and instantaneous phase signal [36], etc., have been successfully applied to RC fault diagnosis. Although good diagnosis results have been achieved, the difficulty of data processing is increased because of the singleness of signal and the few effective fault features.…”

Quantitative Diagnosis Method of Gearbox Under Varying Conditions Based on ARX Model and Generalized Canonical Correlation Analysis

“…From the perspective of the load effect of the system for fault diagnosis and condition monitoring, an excitation-based fault diagnosis method mainly includes a method based on the stator current [17], an instantaneous speed analysis [18], [19], and a torsional vibration analysis [20], which mainly makes fault diagnosis from the perspective of system input, without considering the impact of system input on system output. A response-based fault diagnosis method mainly includes diagnosis method based on vibration application [21], [22], lubrication performance [23], acoustic signal [24], temperature signal [25] and the Hertz deformations [26] which are mainly used for a fault diagnosis and condition monitoring from the system output perspective, and do not care about the impact of the transfer path of response signals on the fault diagnosis.…”