The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and mixed with abundant compounded background noise. To extract the potential excitations from the observed rotating machinery, signal demodulation and time-frequency analysis are indispensable. This work proposes a novel particle swarm optimization-based variational mode decomposition method, which adopts the minimum mean envelope entropy to optimize the parameters (α and K) in the existing variational mode decomposition. The proposed fault-detection framework separated the observed vibration signals into a series of intrinsic modes. A certain number of the intrinsic modes are then selected by means of the Hilbert transform-based square envelope spectral kurtosis. Subsequently, in this study, the feature representations were reconstructed via the selected intrinsic modes; then, the envelope spectra of the real faulty conditions were generated in the rotating machinery. To verify the performance of the proposed method, a testbed platform of a gearbox with a combination of different faults was implemented. The experimental results demonstrated that the proposed method represented the patterns of the fault frequency more explicitly than the available empirical mode decomposition, the local mean decomposition, and the wavelet package transform method.

Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective.

High-precision fault diagnosis is vital for widely used multistage gearbox systems. Intelligent monitoring is difficult due to the fuzzy boundaries and a variety of unseen single or simultaneous faults of such complex machinery. To solve this problem, local mean decomposition (LMD) is applied to extract features effectively from the original non-stationary and nonlinear vibration signals. By exploiting the diverse functionalities of extreme learning machines (ELM) in both regression and classification, a novel dual-ELM network is proposed in which one ELM is employed to count the number of faults and the other is used to identify the specific single or simultaneous-fault scenarios. The proposed dual-ELM-based multi-label classifier does not rely on an empirically specified threshold. Thus, it is more self-adaptive than the existing probabilistic-based classifiers. In addition, by inheriting the advantages of the original ELM, the dual-ELMs do not require iterative fine-tuning of parameters. Finally, the training speed of the dual-ELMs is much faster than other combinations of the existing classifiers. Experimental results under various loading conditions show that the proposed dual-ELM-based fault diagnostic framework is versatile at detecting single and simultaneous faults accurately and quickly.