The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

With the development of information technology, it has become increasingly important to use intelligent algorithms to diagnose mechanical equipment faults based on vibration signals of rolling bearings. However, with the application of high-performance sensors in the Internet of Things, the complexity of real-time classification of multichannel, multidimensional sensor signals is increasing. In view of the need for intelligent methods for fault diagnosis methods of mechanical equipment, the generalization ability of fault diagnosis models also needs to be further strengthened. In this context, in order to make fault diagnosis intelligent and efficient, a bearing fault diagnosis method based on spectrum map information fusion and convolutional neural network (CNN) is proposed. First, short-time Fourier transform (STFT) is used to analyze the multichannel vibration signal of the rolling bearing and obtain the frequency domain information of the signal over a period of time. Second, the information fusion is converted into two-dimensional (2D) images, which are input into CNN for training, and the bearing fault identification model is obtained. Next, the frequency domain information of each signal is converted into a 2D spectrum map, which is used as a CNN training dataset to train a bearing fault identification model. Finally, the diagnostic model is validated using the existing datasets. The results show that the accuracy of fault diagnosis using the proposed bearing is greater than 99.4% and can even reach 100%. The proposed method considerably reduces the workload of the diagnosis process, with strong robustness and generalization ability.

Early in the design process, few computational tools exist to help designers more fully explore a problem's solution space. Existing automated concept generators use knowledge from existing designs and the desired functionality of the new product to suggest solutions. These methods produce many candidate solutions, but often provide the designer with unmanageably large sets of solutions. So techniques are needed to organise the set of solutions into smaller groups. Under the premise that quality designs come from experienced designers, this paper presents a matrix-based automated concept generation method that is intended to analyse traditional conceptual design phase activities. The functional model is defined with the functional basis and a repository containing relationships of sub-functions and artificial components is developed. Morphological matrix, function–component matrix and design structure matrix are structured to produce and rank feasible concept solutions in the early design process. Furthermore, ant colony optimisation is used to optimise the generated conceptual candidate solutions and optimal results are demonstrated to the user automatically. Finally, a software prototype is developed and a case study showing the prototype applied to a design problem for transmission mechanism of loading platform is detailed.

Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.

Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application.