Electrical Discharges in Oil-Lubricated Rolling Contacts and Their Detection Using Electrostatic Sensing Technique

The reliability of rolling element bearings has been substantially undermined by the presence of parasitic and stray currents. Electrical discharges can occur between the raceway and the rolling elements and it has been previously shown that these discharges at relatively high current density levels can result in fluting and corrugation damages. Recent publications have shown that for a bearing operating at specific mechanical conditions (load, temperature, speed, and slip), electrical discharges at low current densities (<1 mA/mm2) may substantially reduce bearing life due to the formation of white etching cracks (WECs) in bearing components, often in junction with lubricants. To date, limited studies have been conducted to understand the electrical discharges at relatively low current densities (<1 mA/mm2), partially due to the lack of robust techniques for in-situ quantification of discharges. This study, using voltage measurement and electrostatic sensors, investigates discharges in an oil-lubricated steel-steel rolling contact on a TE74 twin-roller machine under a wide range of electrical and mechanical conditions. The results show that the discharges events between the rollers are influenced by temperature, load, and speed due to changes in the lubricant film thickness and contact area, and the sensors are effective in detecting, characterizing and quantifying the discharges. Hence, these sensors can be effectively used to study the influence of discharges on WEC formation.

Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA-CAPF Framework

The remaining useful life (RUL) prediction of quay crane (QC) bearings is of great significance to port production safety. An RUL prediction framework of QC bearing under dynamic conditions is proposed. Firstly, the load is discretized, and the corresponding operating conditions are classified. Then, the Autoregressive Integrated Moving Average (ARIMA) model is utilized to predict the load and corresponding operating conditions. Secondly, a Wiener process considering degradation rates and jump coefficients under different operating conditions is developed as the state transfer function. Finally, a condition-activated particle filter (CAPF) is proposed to predict the system state and the bearing’s RUL. The proposed prediction framework is verified by the hoist bearing life cycle data from a port in Shanghai collected by the NetCMAS system. The prediction results by the ARIMA-CAPF framework in comparison with three other prediction strategies identify the effectiveness.

Quality control of adjustment of clearances of ball bearing supports, dynamic devices rotor-type, according to parameters of acoustic emission signals

The article is devoted to increasing the bearing life of dynamic rotary-type machines by controlling the uniformity of the distribution of the value of the preliminary axial load acting on the rolling bearings of the rotation axis of the power unit. The possibility of monitoring the axial load using acoustic emission (AE) signals is considered. The results of experimental studies of the kinematics of the ball movement relative to other bearing parts, depending on the tightening torque of bolted joints, estimated by the parameters of AE signals, are presented.

Recent Patents on Third Generation Bearing Testing Machine

Background: Bearing testing machine is the key equipment for bearing design, theoretical research and improvement, and it plays an important role in the performance of bearing life, fatigue, vibration and working temperature. With the requirements of aerospace, military equipment, automobile manufacturing and other industrial fields of the bearing are becoming higher and higher. There is an urgent need for high-precision and high-efficiency bearing testing machines to monitor and analyze the performance of bearings. Objective: By analyzing the recent patents, the characteristics and existing problems of the current bearing testing machine are summarized to provide references for the development of bearing test equipment in the future. Methods: This paper reviews various representative patents related to the third generation bearing testing machines. Results: Although the structure of bearing testing machines is different, the main problems in the structure and design principle of bearing testing machine have been summarized and analyzed, and the development of trend and direction of the future bearing testing machine have been discussed. Conclusion: Bearing testing machines for health monitoring of bearing life cycle is of great significance. The current bearing testing machine has basically achieved the monitoring and analysis However, due to the emergence of new types of bearings, further improvement is still needed. With the development of testing technology towards intelligent and big data-driven direction, bearing testing machine is moving towards the type of cloud computing and large-scale testing.

Bearing life calculations in rotating and oscillating applications

This paper begins by describing standard bearing life models in continuous rotation before going on to explain how the bearing life can be calculated for roller and ball bearings in oscillatory applications. An oscillation factor a_osc is introduced which accounts for the oscillating and stationary ring. This can be calculated numerically as a function of the oscillation angle θ and load zone parameter ε as well the parameters γ =D·cos(a)/dm and the ball-race osculation factors. Critical angles as used by Rumbarger are also employed at low θ values. Appropriate curve-fitted relationships for both roller and ball bearings are then given for a simple calculation of aosc with an accuracy of approximately 10%. Finally, several methods are suggested for estimating the ε parameter using a real case with a Finite Element Analysis load distribution accounting for structural ring deformation and ball-race contact angle variations. The results derived in this paper allow the lifetime of any arbitrary oscillating ball or roller bearing to be calculated.

Dynamic Influence of Wheel Flat on Fatigue Life of the Traction Motor Bearing in Vibration Environment of a Locomotive

Wheel flat can cause a large impact between the wheel and rail and excites a forced vibration in the locomotive and track structure systems. The working conditions and fatigue life of the motor bearings are significantly affected by the intensified wheel–rail interaction via the transmission path of the gear mesh. In this study, a fatigue life prediction method of the traction motor bearings in a locomotive is proposed. Based on the L−P theory or ISO 281 combined with the Miner linear damage theory and vehicle–track coupled dynamics, the irregular loads induced by the track random irregularity and gear mesh are considered in this proposed method. It can greatly increase the accuracy of predictions compared with the traditional prediction models of a rolling bearing life whose bearing loads are assumed to be constant. The results indicate that the periodic impact forces and larger mesh forces caused by the wheel flat will reduce the fatigue life of the motor bearings, especially when the flat length is larger than 30 mm. Using this method, the effects of the flat length and relatively constant velocity of the locomotive are analyzed. The proposed method can provide a theoretical basis to guarantee safe and reliable working for motor bearings.

Ball Spalling in Rolling Element Bearings: Decrease in Rolling Contact Fatigue Life Due to Inferior Microstructure and Manufacturing Processes

Through hardened steel ball fatigue failure is an atypical mode of failure in a rolling element bearing. A recent full-scale bench test resulted in ball spalling well below calculated bearing life. Subsequent metallurgical analysis of the spalled balls found inferior microstructure and manufacturing methods. Microstructural analysis revealed significant carbide segregation and inclusions in the steel. These can result from substandard spheroidized annealing and steel making practices. In addition, the grain flow of the balls revealed a manufacturing anomaly which produced a stress riser in the material making it more susceptible to crack initiation. The inferior manufactured balls caused at least an 80% reduction in rolling contact fatigue life of the bearing.

Residual Life Prediction of Metro Traction Motor Bearing Based on Convolutional Neural Network

In order to solve the problem that a single type of sensor cannot fully reflect the bearing life information in the process of bearing residual life prediction of metro traction motor, a bearing residual life prediction method based on multi-information fusion and convolutional neural network is proposed. Firstly, the vibration sensor and acoustic emission sensor are used to collect the bearing life signals on the bearing fatigue life test bench. Secondly, wavelet packet decomposition is used to denoise the collected bearing life signal and extract multiple eigenvalues. On this basis, the multiple eigenvalues are normalized, and the bearing degradation trend is analyzed. Finally, the collected bearing life is divided into five stages, and the processed multiple eigenvalues are fused and input into convolutional neural network for training and recognition. The results show that the probability of predicting the stage of bearing life based on multiple eigenvalues and convolutional neural network is more than 98%.