During the measurement of friction force, the measured signal generally contains noise. To remove the noise and preserve the important features of the signal, a hybrid filtering method is introduced that uses the mutual information and a new waveform. This new waveform is the difference between the original signal and the sum of intrinsic mode functions (IMFs), which are obtained by empirical mode decomposition (EMD) or its improved versions. To evaluate the filter performance for the friction signal, ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), and complete ensemble EMD with adaptive noise (CEEMDAN) are employed in combination with the proposed filtering method. The combination is used to filter the synthesizing signals at first. For the filtering of the simulation signal, the filtering effect is compared under conditions of different ensemble number, sampling frequency, and the input signal-noise ratio, respectively. Results show that CEEMDAN outperforms other signal filtering methods. In particular, this method is successful in filtering the friction signal as evaluated by the de-trended fluctuation analysis (DFA) algorithm.

A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.

In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods.

In this paper, a three-dimensional thermoelastic contact model of coated solids with the frictional heat partition considered is developed by introducing a frictional heat partition model. The influence coefficients of the temperature rise, normal displacement and stress components in the three-dimensional thermoelastic contact model are converted from their corresponding frequency response functions (FRFs) with a conversion method based on the fast Fourier transform (FFT), and the FRFs of solids coated with a homogeneous coating subjected to a coupled action of the mechanical loading and the frictional heat flux on its surface are deduced in the frequency domain by introducing a two-dimensional Fourier integral transform. The contact pressure and the frictional heat partition between the two bodies are solved by employing a fast numerical algorithm based on the conjugate gradient method (CGM) and a discrete convolution fast Fourier transformation (DC-FFT). Comparison between the solutions of the present model and those of a thermoelastic contact model in literature is conducted in order to validate the present model. Several specific conclusions on the effect of the sliding speed, thermoelastic properties and thickness of the coating are drawn based on the result of numerical investigation by utilizing the present model.

This study proposes a new method for detecting and diagnosing fault information for rolling bearings. First, a novel method called complete ensemble intrinsic timescale decomposition with adaptive noise is proposed. Compared with the complete ensemble empirical modes decomposition with adaptive noise (CEEMDAN) method, the proposed method reduces the spurious mode, which interferes with the identification of feature frequency in rolling bearings. In addition, when a typical mode mixing signal is decomposed, the mode mixing problem that appears in the adjacent mode components of the CEEMDAN method is also solved. Second, there is only an intrinsic mode function, which mostly reflects the vibration feature of a rolling bearing. We propose a new method to evaluate these mode functions to determine which mode function contains the most useful information. Our proposed method solves the problem using traditional time-domain analysis methods of a single dimensionless parameter with poor stability and low sensitivity. Our method includes sample entropy, kurtosis, the zero-crossing rate, and a correlation coefficient. The proposed method reduces the limitations of each method to select the sensitive component. Finally, we use a simulation signal and an experimental signal to verify the effectiveness of the proposed method.

Aircraft bearings are the supporting part of aero-engines, so their stable operation guarantees engine safety. At present, multisensor information fusion is very common in the fault diagnosis of aviation bearings. Currently, the commonly used monitoring means can only collect information about a single state through each utilized sensor. However, when different sensors are used for the sake of obtaining comprehensive information, this method brings disadvantages such as a reduced computing speed. For this reason, a new aircraft bearing fault measurement method is proposed based on weak magnetic detection. It can realize the synchronous collection of vibration signals and the rotation frequency of the inner ring, cage and rolling element by nondestructive noncontact means; reduce the number of sensors in the process of aircraft bearing monitoring; measure the bearing slip ratio; provide objective analysis of the relationship between bearing fault and slipping; and provide theoretical support for bearing life evolution analysis and optimal design. A simulation study of the theoretical mechanism and an experimental study on an engineering experimental platform are carried out on weak magnetic detection, and the effectiveness of the method proposed in this paper is verified.