In fault diagnosis field, inconsistent distribution between training and testing data, resulted from variable working conditions of rotating machinery, inevitably leads to degradation of diagnostic performance. To address this issue, this study proposes a novel fault diagnosis method based on enhanced multi-scale sample entropies and balanced adaptation regularization based transfer learning. Specifically, different statistics-based multi-scale sample entropies are used to improve feature discriminability for different fault patterns under each working condition and enhance similarity of fault information between different working conditions. Then, based on these hand-crafted features, an improved transfer learning algorithm, referred to as balanced adaptation regularization based transfer learning, simultaneously exploring balanced distribution adaptation and balanced label propagation, is utilized to learn an adaptive classifier to perform cross-domain fault diagnosis. Finally, two public rolling bearing datasets verify that the proposed method can achieve an accurate diagnosis and outperform several existing transfer learning methods.

With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-user’s limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.

Machinery fault diagnosis methods based on dimensionless indicators have long been studied. However, traditional dimensionless indicators usually suffer a low diagnostic accuracy for mechanical components. Toward this end, an effective fault diagnosis method based on redefined dimensionless indicators (RDIs) and minimum redundancy maximum relevance (mRMR) is proposed to identify the health conditions of mechanical components. In the proposed method, the vibration signals are first processed by the variational mode decomposition, and multiple RDIs are constructed based on the decomposed signals. Subsequently, the mRMR approach is introduced to select the RDIs and several important RDIs can be obtained. Finally, the obtained RDIs are fed into a grid search support vector machine to perform fault pattern identification. To verify the superiority of the proposed method, two experimental examples for different fault types of mechanical components including rolling bearing and gearbox are conducted. The experimental results demonstrated that the RDIs as new fault features can effectively solve the deficiency of the traditional dimensionless indicator, and has stronger distinguishing ability for machinery faults. Additionally, our proposed method successfully differentiated 12 fault conditions of rolling bearings and nine fault conditions of gears with average accuracies of 97.47% and 97.12% with 11 and 5 RDIs, respectively.

Industrial heat exchangers are widely used in industries such as petrochemicals, energy and power, and food processing, making them one of the most important pieces of heat and mass transfer equipment in industry. During operation, a layer of fouling often adheres to the heat transfer surfaces, which reduces the heat transfer coefficient of the equipment and increases the thermal resistance of the surfaces. Additionally, fouling can corrode the material of the heat transfer surfaces, compromise their integrity, and even lead to perforations and leaks, severely impacting equipment operation and safety while increasing energy consumption and costs for enterprises. The application of anti-fouling coatings on surfaces is a key technology to address fouling on heat transfer surfaces. This paper focuses on introducing major types of anti-fouling coatings, including polymer-based coatings, “metal material + X”-type coatings, “inorganic material + X”-type coatings, carbon-based material coatings, and other varieties. It analyzes and discusses the current research status and hotspots for these coatings, elaborates on their future development directions, and proposes ideas for developing new coating systems. On the other hand, this paper summarizes the current research on the main factors—surface roughness, surface free energy, surface wettability, and coating corrosion resistance—that affect the anti-fouling performance of coatings. It outlines the research hotspots and challenges in understanding the influence of these three factors and suggests that future research should consider the synergistic effects of multiple factors, providing valuable insights for further studies in the field of anti-fouling coatings.

This paper investigates the failure of a regulating valve stem in a petrochemical plant, which was mainly caused by vibration fatigue under small opening conditions. The fractured valve stem was analyzed using macroscopic analysis, chemical composition analysis, mechanical property analysis, metallographic analysis, fracture surface observation, and energy spectrum analysis. Additionally, fluid-structure interaction (FSI) modal analysis was used to investigate the failure of the regulating valve. The results indicate that the valve opening had a direct impact on the vibration of the valve body, which, when operated at small openings, led to fatigue fracture at the step of variable cross-section. The paper suggests a smooth transition treatment be performed at the variable cross-section of the valve stem to avoid stress concentration. Although this study is limited to a specific case, it provides valuable insights for the failure analysis of valves operating at small openings.

High-pressure steam pipes inevitably suffered from the reciprocal interaction of high pressure and temperature during a long-period service, causing deformation and cracking. However, only limited studies about abnormal bulging caused by condensed water have been carried out. To study the relationship between bulging and condensed water, bulging tee joints belonging to high-pressure steam pipes were investigated with a macro visual inspection, chemical composition analysis, and metallographic microscopy. According to the analysis of the bulging samples, pearlite spheroidization was found in the abnormal bulging tee joint. The ANSYS FLUENT modeling indicated that the tube wall of bulging tees was continuously subjected to alternating stress, causing the cyclic transformation of the liquid–gas phase inside the tee joint. The results indicate that the stress produced by a condensed water droplet ranges from 532.8 MPa to 59 MPa, continuously exerting pressure on the tube wall of the tee joint. When combined with the variation in the temperature field, the temperature of the severe bulging tee joint and slight bulging tee joint alternates. Further modeling illustrates that the stress generated by the impact of condensed water droplets on the high-temperature tee joints causes a ratcheting effect, which is identified as the main factor contributing to the bulging of the tee joint. Deterioration of the microstructure is considered a secondary mechanism.