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Large displacement misalignment under the action of active faults can cause complex three-dimensional deformation in subway tunnels, resulting in severe damage, distortion and misalignment. There is no developed system of fortification and related codes to follow. There are scientific problems and technical challenges in this field that have never been encountered in past research and practices.

This paper adopted a self-designed large-scale active fault dislocation simulation loading system to conduct a similar model test of the tunnel under active fault dislocation based on the open-cut tunnel project of the Urumqi Rail Transit Line 2, which passes through the Jiujiawan normal fault. The test simulated the subway tunnel passing through the normal fault, which is inclined at 60°. This research compared and analyzed the differences in mechanical behavior between two types of lining section: the open-cut double-line box tunnel and the modified double-line box arch tunnel. The structural response and failure characteristics of the open-cut segmented lining of the tunnel under the stick-slip part of the normal fault were studied.

The results indicated that the double-line box arch tunnel improved the shear and longitudinal bending performance. Longitudinal cracks were mainly distributed in the baseplate, wall foot and arch foot, and the crack position was basically consistent with the longitudinal distribution of surrounding rock pressure. This indicated that the longitudinal cracks were due to the large local load of the cross-section of the structure, leading to an excessive local bending moment of the structure, which resulted in large eccentric failure of the lining and formation of longitudinal cracks. Compared with the ordinary box section tunnel, the improved double-line box arch tunnel significantly reduced the destroyed and damage areas of the hanging wall and footwall. The damage area and crack length were reduced by 39 and 59.3%, respectively. This indicates that the improved double-line box arch tunnel had good anti-sliding performance.

This paper adopted a self-designed large-scale active fault dislocation simulation loading system to conduct a similar model test of the tunnel under active fault dislocation. This system increased the similarity ratio of the test model, improved the dislocation loading rate and optimized the simulation scheme of the segmented flexible lining and other key factors affecting the test. It is of great scientific significance and engineering value to investigate the structure of subway tunnels under active fault misalignment, to study its force characteristics and damage modes, and to provide a technical reserve for the design and construction of subway tunnels through active faults.

To provide an overview of the similarity‐based modeling (SBM) technology and review its application to condition monitoring of rotating equipment using features calculated from vibration sensor signals.

Concentrates on the practical capabilities and underlying technology of SBM. Examines the effectiveness of it as an approach to detect and diagnose faults in an electric motor‐driven shaft during variable speed operating conditions.

The SBM is a non‐parametric pattern recognition technology developed by SmartSignal that is applied generally to multivariate condition monitoring problems. A vibration sensor is monitored by first transforming the digitized time domain sensor signal into relevant features over time. These features are monitored continuously in real time to detect any discernable differences from normality. The deviations in turn, produce fault signatures in time‐feature space that aid in fault diagnosis.

Gives information on an approach that employs a multivariate similarity‐based modeling technique to characterize the expected behavior of vibration signal features which enables the detection of incipient faults in rotating machinery.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

Presents a real‐time neural network‐based condition monitoring system for rotating mechanical equipment. At its core is an ARTMAP neural network, which continually monitors machine vibration data, as it becomes available, in an effort to pinpoint new information about the machine condition. As new faults are encountered, the network weights can be automatically and incrementally adapted to incorporate information necessary to identify the fault in the future. Describes the design, operation, and performance of the diagnostic system. The system was able to identify the presence of fault conditions with 100 percent accuracy on both lab and industrial data after minimal training; the accuracy of the fault classification (when trained to recognize multiple faults) was greater than 90 percent.

The objective of the study is to examine the response of reinforced concrete (RC) structures subjected to Near-Fault Ground Motions (NFGM) and highlight the importance of considering various factors including the influence of the relative geographical position of near-fault sites that can affect the structural response during an earthquake.

In this paper, the response of a four-storey RC building subjected to NFGMs with varied characteristics like hanging wall and footwall in conjunction with directivity and the effect of pulse-like ground motions with rupture direction are investigated to understand the combined influence of these factors on the behavior of the structure. Furthermore, the capacity and demand of the structural element are investigated for computing the performance ratio.

Results from this study indicate that the most unfavorable combinations for structural damage due to near-fault ground motion are the hanging wall with forward rupture, the fault normal component of ground motions, and pulse-like ground motions with forward directivity.

The results from this study provide valuable insight into the response of RC structures subjected to NFGM and highlight the importance of considering various factors that can affect the structural response during an earthquake. Moreover, the computation of capacity and demand of the critical beam indicates exceedance of desired limits, resulting in the early deterioration of the structural elements. Finally, the analytical analysis from the present study confirms that the hanging wall with forward ruptures, pulse-like motions, and fling steps are the most unfavorable combinations for seismic structural damage.

The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.

The most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.

The presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.

This study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.

The focus of preventive maintenance (PM) programmes in industry is shifting from a pure statistical basis to online condition monitoring. Examines the shortcomings of statistical‐based PM which are contributing to this shift, and the potential benefits of and current research issues within condition‐based PM. Notes that statistics and quality control techniques will continue to play a critical role in this evolution.

The purpose of this study is to explore the seismic damage mechanism of the Dayemaling Bridge during the Maduo earthquake and discuss the seismic damage characteristics of the high-pier curved girder bridge.

In this study, the numerical simulation method is used to analyze the seismic response using synthetic near-field ground motion records.

The near-field ground motion of the Maduo earthquake has an obvious directional effect, it is more likely to cause bridge seismic damage. Considering the longitudinal slope of the bridge and adopting the continuous girder bridge form, the beam end displacement of the curved bridge can be effectively reduced, and the collision force of the block and the bending moment of the pier bottom are reduced, so the curved bridge with longitudinal slope is adopted.

Combined with the seismic damage phenomenon of bridges in real earthquakes, the seismic damage mechanism and vulnerability characteristics of high-pier curved girder bridges are discussed by the numerical simulation method, which provides technical support for the application of such bridges in high seismic intensity areas.

This Study aims to present the seismic hazard assessment of the earthquake-prone eastern of Iran that has become more important due to its growing economic importance. Many cities in this region have experienced life and financial losses due to major earthquakes in recent years. Thus, in this study the seismic hazard maps and curves, and site-specific spectrums were obtained by using probabilistic approaches for the region.

The seismotectonic information, seismicity data and earthquake catalogues were gathered, main active seismic sources were identified and seismic zones were considered to cover the potential active seismic regions. The seismic model based on logic tree method used two seismic source models, two declustered catalogues, three choices for earthquake recurrence parameters and maximum considered earthquakes and four ground motion predicting (attenuation) models (GMPE).

The results showed a wide range of seismic hazards levels in the study region. The peak ground acceleration (PGAs) for 475 years returns period ranges between 0.1 g in the north-west part of the region with low seismic activity, to 0.52 g in the south-west part with high levels of seismicity. The PGAs for a 2,475-year period, also ranged from 0.12 to 0.80 g for the same regions. The computed hazard results were compared to the acceptable level of seismic hazard in the region based on Iran seismic code.

A new probabilistic approach has been developed for obtaining seismic hazard maps and curves; these results would help engineers in design of earthquake-resistant structures.

Reports how the theorem of the texture “tuned” mask was modified to solve some problems encountered in the automatic faults (including filling bars, oil stains, weft‐lacking and holes) detection and recognition of the plain woven fabrics. These problems are the faults of variable shapes and sizes, those of variable structure and the grey‐level differences in the faults of oil stains. The index of the “tuned” mask in the texture “tuned” mask theorem was modified to converge the variability of the faults, and to elongate the distances between each fault’s average texture energy so that the texture energy in normal texture and in faults can be confined to different fixed ranges. The results show that the optimum texture “tuned” mask found from the modified theorem of the texture “tuned” mask can be used satisfactorily to identify different faults due to structure, shapes and size variation. However, in the case of undertoned oil stains and lower density filling bars, this method may sometimes cause misidentification.