Monitoring the condition of suspension systems is significant to ensure the safe operation of modern railway vehicles. For this purpose, an online modal identification scheme, denoted as Correlation Subset based Stochastic Subspace Identification (CoS-SSI) is proposed in this paper to monitor the suspension conditions. Because of the widespread of the dynamic contact status between wheel and track, especially under faulty suspension cases, the vibration responses measured online exhibit high nonstationarity and nonlinearity. To take into account these characteristics of signals, the input correlation signals for SSI are clustered into several successive subsets according to their magnitudes, on which SSI is implemented one by one. In this way it yields a magnitude adaptive SSI for more reliable and accurate identification. Experimental studies were conducted on a 1/5th scaled roller rig system to verify the effectiveness of the proposed method for suspension monitoring. The experimental results show that the CoS-SSI outperform the conventional SSI in that it produces more reliable and realistic identification for the nonlinear system. Furthermore, the effectiveness of the CoS-SSI was verified experimentally with two faulty suspension faults induced into the system.

Multi-spectral transmission image provides a possibility for the detection of early breast cancer. However, in the process of acquiring multi-spectral transmission images, the recognition of heterogeneities has many difficulties due to the image blur caused by the scattering effect of light source in biological tissues and weak transmission signals. This paper proposes a combination method of modulation-demodulation-frame accumulation technique and pattern recognition to achieve heterogeneous classification. First, the acquisition experiment of the phantom multi-spectral images is designed. Then, the signal-to-noise ratio (SNR) of the image is improved by the modulation-demodulation and frame accumulation technique, and the 14-dimensional feature information (firmness, angular second-order distance, contrast, gray-scale correlation, entropy, inverse gap, smoothness, dissimilarity, consistency, center of gravity, area, perimeter, long diameter of irregular image, and short diameter of irregular image) of the heterogeneous region are extracted from the image with high SNR. Finally, the heterogeneous classification accuracy of different models is compared. The results show that: compared with the classification accuracy of the traditional multi-spectral image classification models, random forest (RF) and extreme learning machine (ELM) models have better classification effect when subdividing the four types of heterogeneity based on the data set of this paper. Among them, the RF and ELM models established by the dataset of four-wavelength combination have the best classification effect, and the classification accuracy rate reaches 100%, second, it is the three-wavelength combined model. The single-wavelength model has the worst classification effect. And the operating efficiency of ELM is significantly higher than RF. In conclusion, the image quality is improved by modulation-demodulation and frame accumulation technique. And compared with the classification accuracy of the traditional multi-spectral image classification models, the RF and ELM models established in this paper have better classification effect, which may promote the application of multi-spectral transmission imaging in early screening of breast tumors.

Operational modal analysis (OMA) is a powerful vibration analysis tool and widely used for structural health monitoring (SHM) of various system systems such as vehicles and civil structures. Most of the current OMA methods such as pick‐picking, frequency domain decomposition, natural excitation technique, stochastic subspace identification (SSI), and so on are under the assumption of white noise excitation and system linearity. However, this assumption can be desecrated by inherent system nonlinearities and variable operating conditions, which often degrades the performance of these OMA methods in that the modal identification results show high fluctuations. To overcome this deficiency, an improved OMA method based on SSI has been proposed in this paper to make it suitable for systems with strong nonstationary vibration responses and nonlinearity. This novel method is denoted as correlation signal subset‐based SSI (CoS‐SSI) as it divides correlation signals from the system responses into several subsets based on their magnitudes; then, the average correlation signals with respective to each subset are taken into as the inputs of the SSI method. The performance of CoS‐SSI was evaluated by a simulation case and was validated through an experimental study in a further step. The results indicate that CoS‐SSI method is effective in handling nonstationary signals with low signal to noise ratio (SNR) to accurately identify modal parameters from a fairly complex system, which demonstrates the potential of this method to be employed for SHM.