In the mechanical transmission system, the gear is one of the most widely used transmission components. The failure of the gear will cause serious accidents and huge economic loss. Therefore, the remaining life prediction of the gear is of great importance. In order to accurately predict the remaining life of the gear, a new type of long-short-term memory neural network with macro-micro attention is proposed. First, some typical time-domain and frequency-domain characteristics of vibration signals are calculated respectively, such as the maximum value, the absolute mean value, the standard deviation, the kurtosis and so on. Then, the principal component of these characteristics is extracted by the isometric mapping method. The importance of fusional characteristic information is filtered via a proposed macro-micro attention mechanism, so that the input weight of neural network data and recursive data can reach multi-level real-time amplification. With the new long short-term memory neural network, the health characteristics of gear vibration signals can be predicted based on the known fusion features. The experimental results show that this method can predict the remaining life of gears and bearings well, and it has higher prediction accuracy than the conventional prediction methods.

Aiming at improving the accuracy of planetary gearbox fault diagnosis, an integrated scheme based on dimensionality reduction method and deep belief networks (DBNs) is presented in this paper. Firstly, the acquired vibration signals are decomposed into mono-component called intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD), and then Teager–Kaiser energy operator (TKEO) is used to track the instantaneous amplitude (IA) and instantaneous frequency (IF) of a mono-component amplitude modulation (AM) and frequency modulation (FM) signal. Secondly, a high dimensional feature set is constructed through extracting statistical features from six different signal groups. Then, an integrated dimensionality reduction method combining feature selection and feature extraction techniques is proposed to yield a more sensitive and lower dimensional feature set, which not only reduces the computation burden for fault diagnosis but also improves the separability of the samples by integrating the label information. Further, the low dimensional feature set is fed into DBNs classifier to identify the fault types using the optimal parameters selected by particle swarm optimization algorithm (PSO). Finally, two independent cases study of planetary gearbox fault diagnosis are carried out on test rig, and the results show that the proposed method provides higher accuracy in comparison with the existing methods.

Bearings are vital parts of many mechanical equipment, the vibration signal analysis of bearings with local defects is important in guiding the fault diagnosis. In this paper, a dynamic analysis method is proposed to investigate the vibration response of the deep groove ball bearings (DGBBs) with local defect using a new displacement excitation function based on the Hertz contact theory and Newton's second law. The DGBB is modeled as a two degrees-of-freedom system, and an additional friction force in the defect zone, the influence of centrifugal force, the gravity of rolling elements, and lubrication traction/slip force between rolling elements and raceway are considered. And this model is used to study the dynamic signals of DGBB under different fault sizes and rotation speeds. Results indicate that the simulation signal has many continuous impacts and change over the time which is closer to the actual situation compared with the one-shot impulse function such as rectangular or half-sine or piecewise function when the rolling elements passed through the defect zone. Finally, the validity of the proposed model is verified by experiments. The simulated and experimental results indicate that the proposed model would achieve a more appropriate and accurate dynamic simulation.

In the realm of Industry 4.0, the Industrial Internet of Things (IIoT) is pivotal for advancing prognostics and health management (PHM) through real-time monitoring of asset conditions. The efficacy of these IIoT systems heavily relies on the precision and reliability of acoustic emission (AE) sensor data. Recognizing the critical dependence of IIoT system functionality on AE sensor capability, this study proposes a novel, systematic framework tailored for PHM applications. Our approach expands the application of the gage repeatability and reproducibility (Gage R&R) technique, focusing on enhancing the reliability of IIoT-AE systems. In the experimental study, AE sensors are deployed to collect data across various operational contexts, including different fault types, measurement positions, operators, speeds, and trial counts. This comprehensive exploration reveals how different contextual factors impact AE sensor capability, thereby facilitating the strategic selection of contexts for precise data acquisition. Additionally, we introduce an innovative three-region graph comprising key metrics, namely, percentage of repeatability and reproducibility (PRR), precision-to-tolerance ratio (PTR), and signal-to-noise ratio (SNR), to provide a clear and intuitive visualization of AE sensor capability and acceptability based on well-defined criteria. Our findings lay the groundwork for ensuring the accuracy and reliability in IIoT systems for PHM, while also providing invaluable guidance for contextual design and practical enhancement of AE sensors, with broader implications for real-time sensor capability evaluations in IoT systems. This work marks a significant step forward in ensuring the reliability of IIoT deployments in PHM, ultimately contributing to the advancement of sensor technology in Industry 4.0 applications.

In condition monitoring, the reliability of a predictive maintenance program is critically dependent on the precision of data obtained from measurement systems. With increased availability, a significant challenge is evaluating the capability of these measurement systems to ensure data precision, which is fundamental for informed system selection. To address this challenge, this study proposes a systematic framework for evaluating the capability of these measurement systems using Gage repeatability and reproducibility (Gage R&R) technique, subsequently judging the acceptability level and guiding their selection to guarantee the data precision. Our study investigates the capability of these systems in terms of repeatability and reproducibility, quantifying the contributions of different sources to the systems’ capability and providing directions for measurement system correction and enhancement. Another distinctive innovation of our approach is the use of three‐region graphs, incorporating metrics including percentage of Gage R&R to total variation, precision‐to‐tolerance ratio, and signal‐to‐noise ratio, which presents a comprehensive overview of the systems’ capability within one single figure. Two comparative experiments in distinct application scenarios were conducted to validate the effectiveness of the proposed framework. The insights presented serve as a valuable reference to replace the commonly used experience‐based system selection in condition monitoring. Through this framework, we present a promising data‐based approach aimed at enhancing the widely employed time‐based calibration strategies, ultimately contributing to the improvement of data quality and the overall success of condition monitoring initiatives.