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The optimization of mining vehicle rear axle weld joints is a complex procedure. The designs for low-cycle fatigue with peak loads were analyzed using computational models and rig testing. On-road evaluation of high-cycle fatigue was performed with the entire vehicle. The iterative analysis is used in design optimization to minimize design cycle time. In sophisticated fatigue calculations with all affecting factors, FEM is utilized. In fatigue failures using road load data, the present deterministic method yields up to 80% correlations. Some critical failure spots were most likely missed owing to operational variations caused by the randomness and multiaxiality of load transfer in link mechanisms with non-linear elements. The fabricated rear axle housing joints are optimized by comparing damage state estimates using stochastic type and multiaxial equivalent loads obtained using Multibody Simulation. The damage state analysis as MBS input stimulus in FE weld models and bolted designs were compared. [ABSTRACT FROM AUTHOR]

The importance of the quality of life of rotating machinery increases the Bearing fault diagnosis. Deep learning models (DL)-based databases become increasingly smart in the field of fault diagnostics, the latest research has widely used CNNs (convolutional neural networks). This paper proposes a new way to diagnose bearing failures with CNN with Bilinear LSTM. Traditional CNNs are however not easy to detect defects due to the fixed geometry of complex fault diagnosis with different working conditions. Our primary and secondary classifiers at specified layers replace primitive shape convolutions with reconfigurable convolutions, resulting in classification results with stringent feature time-frequency incompatibility and a larger receptive field. To acquire more adaptive knowledge and insight into the proposed approach, we employ the CWRU (Case Western Reserve University) opensource dataset to compare classification accuracy. The bearing dataset has been subjected to comprehensive experiments and evaluations in order to confirm the efficacy of the suggested technique's diagnostic performance in a variety of settings. By comparing multiple perspectives on the same dataset with related tasks, the proposed method's superiority is proved. To limit the effect of noise and avoid temporal oscillations, degraded index sequences are matched with a CNN. Current and previous inspection data are fed into a new CNN-BiLSTM model, which is then used to predict the useful time and compatible power values of bearing RULs. When it comes to output, go with the lifetime percentage. The proposed method has been tested by accelerating bearing operation to failure, and the results show that the method has advantages in predicting RUL more accurately. The results of the experiments suggest that the proposed core distance measurement method is a viable new tool for intelligent rolling bearing diagnosis. The BiLSTM technique is more diagnostic than some generic models, according to experimental results using the 48 K and 12 K CWRU datasets, with overall accuracy of 99.80% and 98.3%, respectively. [ABSTRACT FROM AUTHOR]

Ambient day and night noise levels prediction problems have traditionally been addressed using various statistical and machine learning methods. This paper presents the time-series predictions and forecasting of ambient noise levels using support vector machine (SVM) and deep learning method such as convolutional neural network (CNN) approach. This approach has been rarely reported for modeling ambient noise levels so far, although it has been widely used in air and water pollution predictions and forecasting. The study presents the applications of these techniques in time-series modeling of ambient day and night equivalent noise levels. A case study of ambient noise levels of one site each lying in commercial, residential, industrial and silence zone is presented. Ten-fold cross-validation is used in SVM model to train the model effectively and determine the optimized value of hyper-parameter (g, «, C). Also, CNN with a convolutional and pooling layer architecture framework is designed with optimum value of batch size, activation function, and filter size, among others. The validation and suitability of developed SVM and CNN models are ascertained by various statistical tests. Convolutional neural network approach is observed to outperform SVM model and thus can be a reliable approach for time-series modeling of ambient noise levels with a prediction error of 2.1 dB(A). The forecasting root mean squared error obtained for all the four zones using CNN model is observed to be less than 2.1 dB(A) for day equivalent noise levels and 1.9 dB(A) for night equivalent noise levels. [ABSTRACT FROM AUTHOR]