In order to effectively extract the characteristic information of bearing vibration signals and improve the classification accuracy, a composite fault diagnosis method of rolling bearing based on the chaotic honey badger algorithm (CHBA), which optimizes variational mode decomposition (VMD) and extreme learning machine (ELM), is proposed in this paper. Firstly, aiming to solve the problem that the HBA optimization process can easily fall into local optimization and slow convergence speed, sinusoidal chaotic mapping is introduced to improve HBA, and the advantages of CHBA are verified by 23 benchmark functions. Then, taking the Gini index of the square envelope (GISE) as the fitness function, the VMD is optimized with CHBA to obtain the optimal number of modes K and the quadratic penalty factor. Secondly, the first four IMF components with the largest GISE values are selected, and the IMF components are grouped by the “Systematic Sampling Method (SSM)” to calculate the signal energy to form the fault feature vector. Finally, taking the classification error rate as the fitness function, the feature vector is input into the ELM model optimized by CHBA to classify and identify different types of faults. Through experimental analysis, and compared with BP, ELM, GWO-ELM, and HBA-ELM, this method has better diagnosis results for composite faults, and the accuracy of fault classification can reach 100%, which provides a new way to solve the problem of composite fault diagnosis.

Multi-mode processing is a central feature of modern industry. The application of monitoring technology to multi-mode processing is crucial to ensure process safety and to enhance product quality. This paper describes the definition, nature and data characteristics of the multi-mode process. A complete classification framework for multi-mode process monitoring methods is produced, covering steady-state modes and transitional processes. After introducing basic concepts and describing research outcomes obtained for monitoring methods, prospects for multi-mode process monitoring technology are discussed.

Due to the symmetry of the rolling bearing structure and the rotating operation mode, it will cause the coupling modulation phenomenon when it is damaged in multiple places at the same time, which makes it difficult to accurately identify all kinds of faults. For such problems, a compound fault diagnosis method based on adaptive chirp mode decomposition (ACMD), Gini index fusion and long short-term memory (LSTM) neural network optimized by Aquila Optimizer (AO) is proposed. Firstly, a series of IMF components are obtained by decomposing the vibration signal by means of ACMD, and the required components are selected by using the correlation coefficient method. Then, the Gini index of the square envelope (GISE) and the Gini index of the square envelope spectrum (GISES) of each component are calculated, respectively, and they are fused to construct a highly dimensional feature matrix. Then, with the aim of solving the problem of difficult selection of LSTM hyperparameters, the AO-LSTM model is constructed. Finally, the feature matrix is divided into a training set and a test set. The training set is input into the model for training, and then the training network is used to predict the test set, and outputs diagnostic results. The simulation and experimental results show that the proposed method can achieve higher accuracy and stronger robustness, compared with the existing intelligent diagnosis methods for bearing compound faults.

It is difficult for rolling bearings to realize high-precision fault diagnosis with variable speed. To obtain the features of variable speed fault signal effectively and complete the classification work of high accuracy, robust local mean decomposition (RLMD), fractional hierarchical range entropy (FrHRE), hunter–prey optimization algorithm (HPO) and random forest (RF) are combined. Then the paper advances a model for fault diagnosis based on RLMD, FrHRE and HPO-RF. Firstly, RLMD is selected to reconstruct the signal to eliminate some noise interference in this paper. Secondly, FrHRE is chosen to extract the useful feature. Next step, HPO is used to optimize the important parameters of RF and enhance RF’s classification ability. Finally, these obtained features are imported into the optimized RFmodel to achieve the classification. The experimental data is provided by University of Ottawa. The experiment analysis demonstrates that the proposed method performs very well in classification.

Accurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K‐nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10‐fold cross‐validation technique. All these methods reached an accuracy of 95% or above, with F 1 scores and AUC values above 0.96. The support vector machine algorithm outperformed all other models, achieving perfect recognition with 100% accuracy and F 1/AUC scores of 1.0. Our study demonstrates that electronic nose data combined with support vector machine can be used to successfully discriminate between stale and fresh beef, paving the way for novel research directions in the detection of stale beef.

When diagnosing the weak fault of rolling bearing, the fault characteristic is difficult to be extracted because the fault signal has a small amplitude and is susceptible to noise. Aiming at this problem, a fault diagnosis method is proposed based on fractional Fourier transform (FRFT) and deep belief networks (DBN). The original fault signal is first transformed into the fractional domain, and the signal is filtered in this domain to extract the fault features. The characteristic signal is then input to the DBN, and the whole network is optimized to finally realized fault diagnosis by using the pre-training and the reverse propagation algorithm. The simulation results show that the method can effectively detect the weak fault of rolling bearing.