The vibration signal of mechanical equipment in operating environments is the key to describing fault characteristics, but due to thez influence of equipment density and environmental interference, the accuracy of fault diagnosis is often affected by noise. In this paper, a fault diagnosis method based on a 1D Multi-Channel Improved Convolutional Neural Network (1DMCICNN) is proposed. By introducing BiLSTM, an attention mechanism and a local sparse structure of a two-channel Convolutional Neural Network, the feature information of the noisy timing signal is fully extracted at different scales while reducing the computational parameters. The model is verified through experiments under different signal-to-noise ratios and loads. The results show that the accuracy of 1DMCICNN is 98.67%, 99.71%, 99.04%, and 99.71% on different load and speed datasets. Meanwhile, compared with the unoptimized two-channel Convolutional Neural Network, the training parameters are reduced by 55.58%.

The same independent distribution is not obeyed for the data collected under complex working conditions such as time-varying speed or loading, and the fault characteristic information is insufficient, resulting in low accuracy by using traditional methods. To solve the above problem, a fault diagnosis method based on time-frequency joint feature extraction combined with deep learning is proposed. Firstly, the original vibration signal is processed by variational mode decomposition (VMD) to obtain several intrinsic mode functions (IMFs), then the sensitive components are selected by calculating the steepness values of each IMF. Subsequently, the characteristic features of the selected sensitive component in time-domain, frequency-domain and time-frequency domain are calculated to form the time-frequency joint feature. The sparse attention mechanism (SAM) is combined with the advantages of recurrent neural network (RNN) and convolutional neural network (CNN) to form a hybrid deep learning model (SAM-RNN-DCNN). Finally, the time-frequency joint features are combined with the hybrid model for fault diagnosis. Experimental verifications are carried out by using data sets under variable rotational speed, variable load and strong noise interference, and the analysis results show that the proposed method has high diagnostic accuracy, good diagnostic performance and robustness under complex working conditions.

With regard to fault diagnosis of rolling bearing, the envelope demodulation method is usually used to analyze the original vibration signal of faulty bearing, then the fault location of the bearing is determined by examining the distributions of fault characteristic frequencies, harmonics and sideband on the envelope demodulation spectral. The fault characteristic frequency with its harmonics could not be extracted by this traditional method when the original vibration signal is contaminated heavily by background noise. Besides, this method needs high professional knowledge and will expose drawbacks such as complex work, low diagnostic efficiency and so on while dealing with a large number of faulty bearings in engineering application. To solve the above problems, this paper proposes an automatic harmonic feature extraction method. Firstly, a series of bandpass filters are obtained based on fast Kurtogram, and then the original signal is filtered by the series of bandpass filters. The series of filtered signals are subjected to envelope analysis and noise reduction processing. Finally, the denoised series of envelope results are processed by the proposed algorithm for extracting the number of harmonics, harmonic intensity, and harmonic intensity index, and the target feature frequency and its harmonic components hidden in the original signal is extracted automatically. A simulation case and an engineering case verify that the proposed method can not only automatically calculate the number of harmonics of the characteristic frequencies, but also calculate the corresponding harmonic intensity, providing more effective and efficient feature support for fault diagnosis of bearing.

This study focuses on optimizing the flow field uniformity within a vertical honeycomb wet electrostatic precipitator (WESP), which is a critical prerequisite for achieving high particulate removal efficiency. For a vertical honeycomb WESP with an air capacity of 25,000 m³/h, the internal flow field is optimized by adjusting the opening ratio and aperture ratio of the airflow equalizing plate, installing additional deflector plates, and adding additional airflow equalizing plates at strategic locations. The optimization reduces the velocity relative standard deviation at the anode inlet section to 0.14. Through 1:1-scale equipment construction and testing, the particle concentration at the outlet is stabilized below 10 mg/Nm³, with an average removal efficiency of 95.88%—a 5.7% improvement over the original model. This study solves the design dependency on empirical guidance for vertical honeycomb WESP in the food industry, providing a green technology paradigm for low-carbon industrial emissions.

This study focuses on the application of vertical honeycomb wet electrostatic precipitator (WESP) in food processing and animal feed production industries, addressing sustainable development challenges in industrial air pollution control. For a vertical honeycomb wet electrostatic precipitator with an air capacity of 25000 m³/h, the internal flow field is optimized by adjusting the aperture rate and aperture ratio of the airflow equalizing plate, installing additional deflector plates, and adding additional airflow equalizing plates from multiple perspectives. The optimization reduces the velocity relative standard deviation at the anode inlet section to 0.14. Through 1:1 scale equipment construction and testing, the particle concentration at the outlet is stabilized below 10 mg/Nm³, with an average removal efficiency of 95.88%—a 5.7% improvement over the original model. The optimized WESP demonstrates reduced energy consumption by 15%, water recycling efficiency ≥95%, and material recyclability of 90%, aligning with global sustainable development goals (SDGs). This study solves the design dependency on empirical guidance for vertical honeycomb WESP in the food industry, providing a green technology paradigm for low-carbon industrial emissions.