Recently, deep learning has become an effective fault diagnosis method due to its no-mankind feature extraction capability. However, in real applications, rolling bearings are often used in the strong noise and variable working conditions, which lead to the degradation of fault diagnosis ability of neural network model. In order to solve the problem, a self-adaptive deep residual shrinkage network with global parametric rectifier linear unit (DRSN-GPReLU) is presented in this article for intelligent fault diagnosis under variable working conditions, which adopts the DRSN as the basic network architecture. We develop a novel activation function named global parametric rectifier linear unit (GPReLU), which can achieve better intra-class compactness for vibration signals, and the inter-class samples are better mapped into remote areas. Furthermore, a novel network based on the attention mechanism is designed to automatically infer the slope of the GPReLU. Various experimental results demonstrate that the DRSN-GPReLU can realize better performance compared with the traditional methods under variable working conditions, and has better robustness under noise interferences.

Recently, the fault diagnosis domain has witnessed a surge in the popularity of the deep residual shrinkage network (DRSN) due to its robust denoising capabilities. In our previous research, an enhanced version of DRSN named global multi-attention DRSN (GMA-DRSN) is introduced to augment the feature extraction proficiency of DRSN specifically for noised vibration signals. However, the utilization of multiple attention structures in GMA-DRSN leads to an escalation in the computational complexity of the network, which may pose practical deployment challenges. To address this limitation, this paper proposes a lightweight variant of GMA-DRSN, referred to as LGMA-DRSN, building upon our prior work. Firstly, the numerical variation regularity of the adaptive inferred slope parameters in the global parametric rectifier linear unit (GPReLU) is analyzed, where we surprisingly find that a convex parameter combination always occurs in pairs. Based on this convex regularity, the sub-network structure of the adaptive inferred slope with attention mechanism is optimized, which greatly reduces the computational complexity compared to our previous work. Finally, the experimental outcomes demonstrate that LGMA-DRSN not only enhances diagnostic efficiency, but also ensures a high level of diagnostic accuracy in the presence of noise interference, when compared with our prior work.

Accelerated durability evaluation aims to quantify the fatigue damage of automotive components in a shorter time. The quality of accelerated editing directly affects the evaluation’s efficiency and accuracy. In this paper, an index-controlled accelerated spectrum editing method is proposed, and the equivalent fatigue damage model of the component is derived. Taking an automotive steering knuckle as the verification object, the proposed accelerated editing method is evaluated from multiple perspectives. The evaluation results show that the proposed method has apparent advantages in compression efficiency, damage consistency, and retaining the frequency domain impact on the component.

Summary This study addresses the event‐triggered output feedback control problem of oxygen excess ratio (OER) for polymer electrolyte membrane fuel cell system subject to unknown uncertainties, rapid load variations and unavailable variables. The main contributions consist of the following three aspects. First, a high‐order extended state observer with a variable observer bandwidth is specially designed to exactly reconstruct the unavailable cathode pressure and OER. Second, a nested adaptive law‐based disturbance observer is constructed to estimate the lumped uncertainty, which avoids overestimation and the need for exact upper bounds of the lumped uncertainty and its derivative. Third, to reduce the network communication burden in the controller‐to‐actuator channel and smooth the control signal, a new event‐triggered integral terminal sliding mode control scheme using dynamic gain technique is developed to regulate the OER to its desired value. More comprehensive simulation results (Case B) show that satisfactory estimation and control for OER are achieved under parameter uncertainties, measurement noise and time‐varying disturbances. The performance indexes including the root mean square error and the mean relative error of the proposed controller are 0.567% and 3.074%, respectively, which are smaller than those of the conventional event‐triggered controller and the time‐triggered controller with reduced triggering frequency. Besides, the proposed controller not only saves communication resources without much degradation of control performance but also significantly alleviates the chattering and jump phenomena of control input.

Appropriate control for a polymer electrolyte membrane fuel cell air feeding system is vulnerable to load current disturbance, parameter uncertainties, measurement noise, faulty sensors, and limited communication resources. Considering these practical issues, this study presents a novel estimation‐based event‐triggered nonlinear control scheme without pressure sensors to regulate the oxygen excess ratio (OER) at its optimal reference. First, to exactly reconstruct the unmeasured OER, a uniform robust exact differentiator and an adaptive high gain observer are developed to estimate the supply manifold pressure and the cathode pressure, respectively, thus redundant sensors and development cost can be saved. Then, a reduced‐order high gain observer is constructed to approximate the unmodeled lumped disturbance consisting of external disturbances and system uncertainties, which helps to decrease the control gain and improve the robustness. Further, an event‐triggered adaptive terminal sliding mode controller with disturbance compensation is proposed to guarantee accurate and fast OER tracking control with integral absolute error (IAE) of 0.447 and average settling time of 0.9 s while avoiding chattering effect and unnecessary communication in the controller‐to‐actuator channel. The comparison results illustrate that the proposed approach achieves superior estimation and control of OER with satisfactory robustness to parameter perturbations (less than 4% deviation in IAE).