Locomotive Bearings Research Articles

Feature Denoising and Nearest–Farthest Distance Preserving Projection for Machine Fault Diagnosis

It is a big challenge to identify the most effective features for enhancement of fault classification accuracy in rotating machines due to nonstationary and nonlinear vibration characteristics of the machines under varying operating conditions. To find discriminative features, a novel dimension reduction algorithm, referred to as the nearest and farthest distance preserving projection (NFDPP), is proposed for machine fault feature extraction and classification. With the NFDPP, both the nearest and farthest samples of the data manifold can be analyzed simultaneously to identify features leading to fault classification. Additionally, we denoise the features directly in the feature space to save computation time and storage space, and prove its equivalence to denoising the signals in the time domain. Through analysis of measured vibration data for bearings with different defects, it is demonstrated that the proposed NFDPP approach can effectively classify different bearing faults and identify the severity of the bearing ball defect, and the direct denoising of features yield a significant improvement in fault classification. The effectiveness of the proposed method is further validated in identifying compound faults in locomotive bearings in an industrial setting.

Sparsity-based algorithm for detecting faults in rotating machines

This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.

Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis.

When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks.

The Study of the Processing Method of Room Temperature Vulcanized Silicone Rubber (RTV) on the Surface of the Locomotive Bearing Seal Rings

The experimental research is confirmed the method about the residual room temperature vulcanized Silicone Rubber (RTV) on the surface of the locomotive bearings after dismantling. The Alkaline inorganic solution has no actual effect on the RTV soaking. While the carbinol and xylene organic solution have alittle effect of dissolving for the RTV on the locomotive bearing seal rings, and both reduce elasticity and function of the seal rings. After long time soaking with the acetonum solution and polishing by hand, It can clean up all the RTV, and the seal ring has no effect on it.

A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.

Stator Current-Based Locomotive Traction Motor Bearing Fault Detection

Rolling-element bearings are critical components in locomotive traction motors. A reliable online bearing fault-diagnostic technique is critically needed to prevent motor systems performance degradation and malfunction. Motor bearing failure induces vibration, resulting in the modulation of the stator current. Compared with conventional monitoring techniques such as vibration monitoring or temperature monitoring, stator current-based monitoring offers significant economic benefits and implementation advantages. In this paper, a novel approach to locomotive traction motor current signature analysis based on wavelet packet decomposition (WPD) of stator current is presented. The effectiveness and practicability of the proposed method is verified by locomotive running tests.

Study on Injecting Forming Technology of Railway Locomotive Bearing Retainer

According to the superiority performance, the applications of engineering plastics tend to be widely used day by day in the field of railway transport. The research of locomotive-bearing retainer is an important technology that should be resolved proposed by the Ministry of Railways’ report on the outline of the Eleventh Five-Year Plan. In this paper, the characteristics of each process of the injection molding in the different stage, the main technology parameters, and the influence of the primary process parameters on the part manufacture quality were systematically analyzed. And the modern injecting forming software and new technology were applied to analyze the injecting forming technology, to design the mould structure, to forecast the possible defects and breakdown in the process of injecting forming with the thematically design and the CAE technology. The study results can be extended to the design of other similar parts, which has very strong engineering applications significance.

Design of Status Monitor System for the Locomotive Bearings Based on WSN

The rolling bearing is one of the key parts of the locomotive running components, because it’s running status is directly related to the performance and safety of locomotive. In this paper, ZigBee Wireless Sensor Network (WSN) technology is introduced. The framework，hardware circuit and software layer of the sensor node was researched in detail. The characters of chips and the realization of Zigbee protocol stack also described．Then, the status monitoring system for the locomotive bearings based on WSN is designed. It can monitor the work status of the rolling bearing and realize to alarm for instant fault. This system can also be improved for the other vehicle.

NOVEL HYBRID CLUSTERING ALGORITHM AND ITS APPLICATION TO FAULT DIAGNOSIS

Aiming at the fuzzy C-means(FCM)clustering algo- rithm supposing the uniform influence to clustering by different features and samples,and setting the cluster number beforehand, a novel hybrid clustering algorithm based on 3 layer forward neural networks(FNN),an algorithm of distribution density function of data point and the cluster validity index is proposed. Feature weighting and sample weighting are considered in this hybrid clustering algorithm and the cluster number is automati- cally set by using the cluster validity index to finish clustering. Feature weights are adaptively learned via FNN with the gradi- ent descent technique under the unsupervised mode of training, and sample weights are computed through the algorithm of distribution density function of data point.Then,the feature weights and the sample weights are assigned to the correspond- ing features and samples to emphasize the leading effect of sensitive features and typical samples,and weaken the interfer- ence of other features and samples.The proposed algorithm is employed to analyze the benchmark data and the practical data from locomotive bearings,and the results show that the algo- rithm enables to automatically and correctly set cluster number and its clustering performance is better than that of the FCM.

Determination of the Effectiveness of Lubricants for Locomotive Bearing Units by Means of the Acoustic-Emission Method

The theoretical fundamentals and experience of using the acoustic-emission method for estimating the effectiveness of lubricants for bearing units is considered. The results of practical investigations of the effect of the presence and watering of lubricants on the acoustic emission of a bearing unit are given. It is proposed to go over to repressing the lubricant into the bearing unit on the basis of the results of acoustic-emission testing.

Designing zinc alloy bearings

The zinc-aluminum alloys are capable of being used as replacements for bronze alloys in a variety of bearing applications, often at lower cost. Improved wear resistance and lack of environmental impact are two critical reasons for conversion to zinc bearings. The design of cast zinc-aluminum (ZA) alloy continuous rotation bearings for high load, low speed applications is discussed. Selection criterion include alloy type, casting process, lubrication regime, journal hardness and roughness, clearances, speed and load, running temperature, and wear rate. An example of the design procedure for a locomotive bearing is presented.