What's new

In recent years, power equipment has been developing towards low-carbon, high-efficiency, and green environmental protection. The double reheat unit has been increasingly employed in power plants due to its advantages of low energy consumption and less pollution. As a core component of power plants, the dynamic performance analysis of the steam turbine foundation is essential for ensuring the overall safety of double reheat unit. For this reason, the dynamic performance of a steam turbine foundation is investigated based on the engineering background of frame-type reinforced concrete foundations of 1000 MW double reheat steam turbine set in a power plant. The solid finite element model of the steam turbine foundation is first established by using ANSYS software, along with a detailed description of foundation information and modelling methodology. Subsequently, the dynamic characteristic and response analyses of the steam turbine foundation are performed to evaluate its dynamic performance, respectively. The results indicate that the 1000 MW steam turbine foundation demonstrates satisfactory dynamic performance. Within the operating speed range, the transverse, longitudinal, and vertical vibration displacements of the foundation bearings and columns remain below 20 μm, while the vibration velocity does not exceed 3.8 mm/s, both of which comply with relevant specifications. Moreover, enhancing the stiffness of the fifth and sixth beams, along with increasing the cross-sectional area of columns C3 and C4 on the steam turbine foundation, should be considered to mitigate its vibration responses and thus improve its dynamic performance. The research findings can serve as a reference for the type selection and optimization design of 1000 MW double reheat steam turbine foundations.

Quantitative assessment of risk matrix through analysis of reliability, availability and maintainability (RAM) is used as quick visual tool for managing potential risk in any continuous production system which can be used for further improved maintenance planning. Fault tree analysis along with failure mode and effect analysis support in assessing risk of minor or major failures associated with different consequences of human impact, production loss, maintenance loss etc. For developing risk matrix, scoring of likelihood and severity are necessary to identify the potential risk zone. An attempt has been made in the present study to assess overall failure scenario of offset-printing machine by analysing reliability of different machine component. Different types of failure frequencies and corresponding failure probability of the machine are set as a value representative likelihood failure data. The critical consequences of these failures are discussed for estimation of actual risk and risk index. Matrix of risk and risk priority number is developed here on the basis of likelihood scores of each kind of failure probability and severity scores by considering different types of breakdown and their associated responsible machine component. Moreover, prioritization of different failure types is validated by MonteCarlo simulation. Based on the risk matrix developed, maintainability and maintenance interval time has been determined which seems to be a novel approach for reduction of risk and breakdown time. Finally, maintenance and safety recommendation on the basis of corresponding risk level and maintainability indicator rating are discussed.

In this study, a novel multi-sensory data fusion approach is developed for real-time tool wear condition monitoring during the turning process, addressing the limitations of single-sensor systems that often suffer from noise and uncertainty. By integrating data from four distinct sensors – machine vision, electrical current, accelerometer, and strain gauge – this method enhances the reliability and robustness of wear state identification. Key features extracted include the entropy of the workpiece’s surface texture via stationary wavelet transform, the time-frequency marginal integral of the motor current, and the Shannon entropy of both the cutting tool’s bending strain and acceleration signals. These features are fused using K-means clustering with Lloyd’s algorithm to classify tool wear into three distinct categories: low (0-0.1 mm), medium (0.1-0.2 mm), and high (> 0.2 mm). Experimental results demonstrate that this approach achieves a classification accuracy of 95 %, significantly outperforming traditional single-sensor methods, which typically yield accuracies below 80 %. This scalable and efficient technique is well-suited for intelligent manufacturing, offering precise tool replacement decisions with minimal computational overhead.

Single Point Incremental Forming (SPIF) represents a transformative shift in sheet metal manufacturing, offering unparalleled flexibility, reduced tooling costs, and adaptability for low-volume and customized production. This study presents a hybrid numerical-experimental investigation of SPIF applied to thin circular galvanized plates, integrating finite element simulations via FormingSuite software with experimental validation. A key innovation of this work lies in its detailed analysis of coupled deformation modes, tensile-tensile and tensile-compressive, governing failure and thinning mechanisms under high forming angles, particularly around 90°, which are typically fracture-prone. The research introduces a novel use of the Fracture Forming Line (FFL) over traditional Forming Limit Curves (FLCs) to better predict failure in SPIF. Results reveal improved strain capacity and process stability, supported by deformation mapping, safe zone identification, and stress-strain simulations. This comprehensive approach not only enhances the predictive capabilities of SPIF modeling but also supports smarter and more sustainable manufacturing processes.

This study investigates the application of artificial neural networks for the detection of compressors fouling degradation in industrial gas turbines during operation to mitigate the loss in engine performance. An Artificial Neural Network (ANN)-based model was developed to monitor and predict compressor fouling degradation in an aero-derivative gas turbine derived from the Siemens SGT 400 class of gas turbines. Performance data from a Siemens SGT 400 gas turbine unit were obtained and used for the investigation. The obtained engine data represent all faults indicative of compressor performance. For the baseline, data were collected after maintenance actions had taken place, while the degraded case covers historical engine performance from 01 January 2013 to 28 February 2013, accounting for approximately 1,392 Equivalent Operating Hours (EOH). The dataset, encompassing variables such as temperature, pressure, gas flow, power, compressor discharge temperature, and compressor discharge pressure, was processed to eliminate irrelevant and redundant parameters before usage. A Multi-Layer Perceptron (MLP) was chosen as the architecture for the ANN. The outcomes of the training phase showed that the ANN achieved a classification accuracy of 96.2 % in proficiently distinguishing between “fouling” and "other factors" conditions. Additionally, the validation performance plot demonstrates that the network achieved its best performance with a value of 0.077507 at 18 epochs out of 24 training iterations. Finally, the confusion matrix demonstrates the model's capability to predict both fouling and non-fouling scenarios with a minimal rate of misclassification.

The double earthquake that struck Türkiye on February 6, 2023 killed over 50,000 people. It also caused the collapse of thousands of low-rise reinforced concrete structures in the seismic area, resulting in incredible damage. To study the reasons for the progressive collapse of numerous buildings, a nine-story concrete frame structure was established in this study, and the elastic-plastic time-history analysis under the double earthquake was performed. The element failure criterion was defined using the max equivalent compressive strain, refining the damage initiation point and transmission path of the proposed structure. The results showed that: 1. The natural period of vibration of the low-rise concrete frame structure in the seismic area is in the peak spectral acceleration region of the first earthquake, which maximizes the earthquake damage force. 2. The first strong earthquake disabled the main load-bearing columns on the ground floor of the structure, resulting in collapse within a few seconds of the second earthquake. 3. The seismic damage investigation showed that the low-rise frame structure had insufficient longitudinal reinforcement laps, insufficient transverse reinforcement and stirrups, and weak embeddedness between longitudinal bars and concrete. These issues have amplified the damage degree. 4. Despite the comprehensive building seismic regulations in Türkiye, the most pressing problem may be the long-term regulatory failure of the authorities.

Wheeled mobile robots (WMR) are unmanned vehicles and practical robots for industry and human life. The low-cost manufacturing, simple assembling, high-speed, lightweight design, and high observability and controllability of the WMRs have attracted the attention of engineering disciplines such as mechanical and electrical science. This paper focuses on the control of wheeled mobile robots through fuzzy adaptive back stepping (ABS). The mathematical model of WMR is divided into two types, including kinematic and dynamic analyses. Actually, this research analyzes the theoretical math model using hybrid methods such as fuzzy logic and adaptive back stepping (BS) to control WMR in both noisy and noiseless conditions along its path. On the other hand, this hybrid controller, because of its more robust performance, can track WMR on its targets. Because of this, WMR's ability to move around makes it choose fuzzy and adaptive back stepping (FABS) methods, which use model-based and time-dependent features, respectively. As a result, the signal inputs fuzzy membership functions, and then the fuzzy approach outputs a new signal that goes to the back-step adaptive controller to finalize the control effort to navigate WMR with the lowest error during its destinations.

Impact from falling objects can easily cause the local deformation of pipeline, which threatens the safe and stable operation of pipeline. In order to study the dynamic response behavior of impacted buried pipelines in cold regions, the buried pipelines, frozen soil and falling objects are taken as the object. Considering the nonlinearity of pipeline material, the contact nonlinearity between pipeline, falling objects and frozen soil, a double nonlinear dynamic analysis model of buried pipeline in cold regions is established by explicit dynamic analysis method. The rationality of the model method is verified by comparing the curves in this paper with those from the experiment. Furthermore, the changing laws of dynamic response of pipeline influenced by different factors are discussed. The results show that: when the buried depth of pipeline is 2 m, the deformation and residual stress of pipeline increase with the increase of pipeline’s diameter-to-thickness ratio, the impact velocity of falling object and the water content of frozen soil, and the impact velocity of falling objects influences the dynamic response behavior of pipelines most significantly, followed by the diameter-thickness ratio of pipelines and the water content of frozen soil; When the diameter-thickness ratio of the pipeline is 58, the deformation and residual stress of pipeline decrease with the increase of buried depth by 75 % and 88 % respectively. Among the four influencing factors, when the impact velocity of falling objects is 10 m/s and the buried depth of pipeline is 3 m, the deformation amplitude of pipelines caused by falling objects is the smallest. It is suggested that in the high-risk regions of falling objects, the diameter-thickness ratio, buried depth and the water content of frozen soil can be reasonably controlled under the condition of predicting the maximum potential impact velocity of falling objects, so as to improve the ability of the pipeline to resist external impact damage, which provides theoretical basis and quantitative control standards for the impact design of pipeline engineering in cold regions.