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Superconducting pinning maglev train, with the advantages of heavy self-stable levitation and super-low resistance, is expected to become a high-speed transit in the future. With the increase in speed, the more will the irregularity of the PMG (permanent magnet guideway) affects the safety and comfort of the superconducting pinning maglev train. This paper is based on the PMG of the test line for the first high-speed superconducting pinning maglev engineering prototype train in China, to measure and study the magnetic irregularity of the PMG and its characteristics. Experiments were designed to measure the irregularity with a single Dewar maglev system, which can obtain the equivalent geometric irregularity directly acting on the maglev unit of the vehicle. Firstly, the frequency response function is developed based on previous studies, which can transform the response signal of the Dewar into the irregularity signal of the PMG. Secondly, the velocity and position are measured by image recognition of ground markers. Then, the dynamic signal of the Dewar maglev system moving along the PMG is gained and conversed into equivalent geometric irregularities by the frequency response function. Finally, an equivalent geometric irregularity PSD (power spectral density) function is formed, and it can generate the geometrically random track irregularity for vehicle dynamic simulations. The dynamic simulation result suggests that the superconducting pinning maglev train has the potential for smooth operation at high-speed under the excitation of the current PMG. The studies of this paper can help to understand the irregularity of the PMG further, and the gained PSD function can provide a general reference for the dynamic study of the superconducting pinning maglev train. [ABSTRACT FROM AUTHOR]

• Classified vibration signals to detect leakages in real-life pipe networks. • Implemented automated detection using accelerometers and machine learning methods. • Compared applied models based on multiple criteria against SqueezeNet. In addition to the issues of water quality and economic losses, leakages in pipe networks are a potential danger to public safety. Early detection of water leaks in pipe networks is crucial for implementing countermeasures to reduce structural damage and water losses. The method of data collection based on sensors installed in pipeline networks has gained a lot of attention due to its potential for application in real-time monitoring systems for leak detection. However, many models developed on data collected from simulations, engineered tests or field experiments, and few have been validated using real network data. To make further investigations in real water distribution systems (WDSs), machine learning (ML)-based leak detection models were developed and validated using signal data collected by piezoelectric accelerometers installed in WDSs over several cities of China. Nonlinear features describing the non-stationary characteristics contained in leak signals are extracted from the signals to build feature sets as inputs to the common classifiers, i.e., support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). In addition, the spectrograms obtained by the short-time Fourier transform processing are used for the convolutional neural network SqueezeNet. The effects of different features, different dataset compositions, and different models for leak detection are compared and analyzed. SqueezeNet has the best performance with 95.15% accuracy in identifying leaks, and KNN is the best of the three classifiers with superior sensitivity and 88.17% accuracy in identification. This paper demonstrates the effectiveness and practicability of using machine learning models to identify leaks in real pipe networks. The application of machine learning models can perform effective leak detection with minimal human intervention, thus providing a meaningful contribution to the study of leak detection in real WDSs. The proposed model aims to identify leakages under operational conditions in real-life pipe networks with higher efficiency and reduced manual involvement. This paper demonstrates the applicability of accelerometers based on microelectromechanical systems for leak detection and establishes real network-based machine learning models; thereby, providing meaningful contribution to the literature regarding leak detection in a real WDS. [ABSTRACT FROM AUTHOR]

Vibration testing of long span bridges is becoming a commissioning requirement, yet such exercises represent the extreme of experimental capability, with challenges for instrumentation (due to frequency range, resolution and km-order separation of sensor) and system identification (because of the extreme low frequencies). The challenge with instrumentation for modal analysis is managing synchronous data acquisition from sensors distributed widely apart inside and outside the structure. The ideal solution is precisely synchronised autonomous recorders that do not need cables, GPS or wireless communication. The challenge with system identification is to maximise the reliability of modal parameters through experimental design and subsequently to identify the parameters in terms of mean values and standard errors. The challenge is particularly severe for modes with low frequency and damping typical of long span bridges. One solution is to apply ‘third generation’ operational modal analysis procedures using Bayesian approaches in both the planning and analysis stages. The paper presents an exercise on the Jiangyin Yangtze River Bridge, a suspension bridge with a 1385 m main span. The exercise comprised planning of a test campaign to optimise the reliability of operational modal analysis, the deployment of a set of independent data acquisition units synchronised using precision oven controlled crystal oscillators and the subsequent identification of a set of modal parameters in terms of mean and variance errors. Although the bridge has had structural health monitoring technology installed since it was completed, this was the first full modal survey, aimed at identifying important features of the modal behaviour rather than providing fine resolution of mode shapes through the whole structure. Therefore, measurements were made in only the (south) tower, while torsional behaviour was identified by a single measurement using a pair of recorders across the carriageway. The modal survey revealed a first lateral symmetric mode with natural frequency 0.0536 Hz with standard error ±3.6% and damping ratio 4.4% with standard error ±88%. First vertical mode is antisymmetric with frequency 0.11 Hz ± 1.2% and damping ratio 4.9% ± 41%. A significant and novel element of the exercise was planning of the measurement setups and their necessary duration linked to prior estimation of the precision of the frequency and damping estimates. The second novelty is the use of the multi-sensor precision synchronised acquisition without external time reference on a structure of this scale. The challenges of ambient vibration testing and modal identification in a complex environment are addressed leveraging on advances in practical implementation and scientific understanding of the problem. [ABSTRACT FROM AUTHOR]

Building materials machinery equipment play an important role in the production of cement, brick and tile, glass and other building materials, which are high energy consumption industries. Due to advanced sensors, continuous improvement of signal acquisition technologies and increasing data storage space, a large amount of data can be used by scholars, which makes data-based fault diagnosis gradually studied by more and more scholars. With increasing amount of data, new challenges are as follows: there is very little data that can really be used; the research on compound fault diagnosis and weak fault diagnosis is still not mature; the diagnosis accuracy of variable speed components is low. These problems restrict the further development of fault diagnosis. In this paper, the characteristics of fault diagnosis of building materials equipment are first expounded, the principles and characteristics of main building materials equipment, signal classification, sensor selection and error correction are briefly introduced, then the research status are discussed, the existing difficulties and challenges are summarized, and the potential development directions and trends in this field are given. • Equipment fault diagnosis in high energy consumption industries in China is reviewed. • Common signal types, measurement points, sensor selection and error correction are reviewed. • Current research of equipment fault diagnosis is summarized. • Difficulties and challenges in mechanical fault diagnosis is analyzed. [ABSTRACT FROM AUTHOR]

In the high-speed railway industry, the pantograph-catenary system is responsible to provide continuous electric energy for the high-speed train. The pantograph-catenary system suffers multiple impacts from the complex work environment. The vehicle-track excitation is one of the normal disturbances to the pantograph-catenary interaction. Previous studies only consider the vertical effect of the vehicle-track vibration on the pantograph-catenary interaction. To address this deficiency, both of the pantograph-catenary and vehicle-track models are constructed in this paper. The validations of both models are verified by the experimental test and the world benchmark, respectively. The pantograph base follows the translations and rotations of the car-body caused by random rail irregularities. In combination with a spatial contact model between the contact wire and the pantograph strip, the spatial vibration of the carbody can be fully considered in the pantograph-catenary interaction. The statistical analysis, stochastic analysis and frequency analysis are performed to make sense of the effect of the random track irregularities on the pantograph-catenary interaction. The deviation of the contact point away from the strip centre caused by the carbody vibration is also analysed. The results show that the reliability of the pantograph-catenary system shows a continuous decrease in the degradation of rail quality. The carbody vibration may cause the de-wirement of the pantograph in extreme conditions. Finally, an application example is given to evaluate the dynamic performance of the pantograph-catenary system running on the China high-speed network with realistic rail irregularities. [ABSTRACT FROM AUTHOR]

• An algorithm is proposed to identify track irregularities using vehicle responses. • TThe VB interaction is taken into account in the proposed algorithm. • A railway bridge is employed as an illustrative example to validate its accuracy. Track irregularities affect the running safety of railway vehicles and ride comfort, hence track irregularity identification using the dynamic responses of in-service vehicles is of great interest. Because the high-speed rail lines mainly consist of bridges in China, vehicle-bridge (VB) interactions which significantly influence the vehicle dynamic responses should be taken into account in the track irregularity identification. This paper proposes a Kalman filter algorithm to identify the track irregularities of railway bridges using vehicle dynamic responses considering the VB interactions in real-time. A state space model is established to represent a time-dependent VB system subjected to unknown track irregularity excitations. A Kalman filter algorithm is proposed to estimate optimally the state vector of the VB system and to identify the track irregularities subsequently. Two numerical examples including a real railway bridge constructed in China are presented to validate the accuracy of the proposed algorithm. A parametric study is also conducted to demonstrate the effects of measurement noise, vehicle running state, parameter uncertainty and model uncertainty on the identification of track irregularities. Comparison results demonstrate that the proposed track irregularity identification algorithm outperforms the conventional approaches mainly because of considering the VB interaction. The proposed algorithm enables efficient monitoring the track irregularities of railway bridges using the acceleration responses of in-service vehicles. [ABSTRACT FROM AUTHOR]