Railway track support condition assessment—Initial developments on a vehicle-based monitoring solution through modal analysis

Nowadays, there are multiple initiatives showing a renewed interest on railway transport of goods and passengers around the world. Thus, an efficient management of railway infrastructures, both at the operational level and in terms of economic profitability, is not only desirable but also corresponds to an area of ongoing research. In order to contribute to these efforts, an alternative and novel methodology to evaluate railway track support conditions is presented here, based on modal analysis of the characteristic frequencies of the multi-element system composed by a railway infrastructure and an instrumented vehicle moving over it. This methodology belongs to the group of vibration-based structural damage identification methods, and is focused on observing the characteristic frequencies of this multi-element system, which can be correlated with changes in the physical properties of the railway infrastructure under analysis. An important feature of the proposed methodology is that it should enable the collection of information regarding the conditions of the substructure of a railway infrastructure. By performing this assessment of a railway infrastructure over its length, and over time by comparing different rides over the same railway stretch, important information can be gathered regarding the support conditions of the track. This paper presents a complete description on the current stage of development of the proposed methodology, along with the theoretical model that serves as the basis to interpret the collected data. Preliminary verification of this methodology is performed through the analysis of two case studies regarding the passage of an instrumented vehicle over two underpasses. The results obtained so far show that the proposed methodology can provide relevant information regarding the support conditions of railway tracks.

Optical conductivities in triple fermions with different monopole charges

We investigate the linear optical conductivities of the newly-discovered triple-component semimetals. Due to the exactly flat band, the optical conductivity relates to the transition between the zero band and the conduction band directly reflecting the band structure of the conduction electrons in contrast to the other materials. For the low-energy models with various monopole charges, the diagonal conductivities show strong anisotropy. The ω-dependence of interband conductivities for a general low-energy model is deduced. The real part of the interband σ_xx always linearly depends on the optical frequency, while the one of σ_zz is proportional to ω^{2/n-1}. This can be a unique fingerprint of the monopole charge. For the lattice models, there also exists the optical anomalous Hall conductivity, where a sign change may appear. The characteristic frequencies of the kink structures are calculated, strictly. Our work will help us to establish the basic picture of linear optical response in topological triple-component semimetals and identify them from other materials.

Fault detection via sparsity-based blind filtering on experimental vibration signals

Detection of bearing faults is a challenging task since the impulsive pattern of bearing faults often fades into the noise. Moreover, tracking the health conditions of rotating machinery generally requires the characteristic frequencies of the components of interest, which can be a cumbersome constraint for large industrial applications because of the extensive number of machine components. One recent method proposed in literature addresses these difficulties by aiming to increase the sparsity of the envelope spectrum of the vibration signal via blind filtering (Peeters. et al., 2020). As the name indicates, this method requires no prior knowledge about the machine. Sparsity measures like Hoyer index, l1/l2 norm, and spectral negentropy are optimized in the blind filtering approach using Generalized Rayleigh quotient iteration. Even though the proposed method has demonstrated a promising performance, it has only been applied to vibration signals of an academic experimental test rig. This paper focuses on the real-world performance of the sparsity-based blind filtering approach on a complex industrial machine. One of the challenges is to ensure the numerical stability and the convergence of the Generalized Rayleigh quotient optimization. Enhancements are thus made by identifying a quasi-optimal filter parameter range within which blind filtering tackles these issues. Finally, filtering is applied to certain frequency ranges in order to prevent the blind filtering optimization from getting skewed by dominant deterministic healthy signal content. The outcome proves that sparsity-based blind filters are effective in tracking bearing faults on real-world rotating machinery without any prior knowledge of characteristic frequencies.

Beyond the brain: towards a mathematical modeling of emotions

This article focuses on a mathematical description of the emotional phenomenon. The key concept is to consider emotions as an energy, and to rely on the analogy with the electromagnetic waves. Our aim is to provide a mathematical approach to characterize the emergence of emotional fluxes in the human psyche. This goes beyond classical pscychological approaches. In this setting, specific emotions correspond to specific frequencies and our psychic state results from the summation of different characteristic frequencies. Our general model of psychic state is a dynamical system whose evolution results from interactions between external inputs and internal reactions. The model provides both qualitative (frequencies) and quantitative (intensity) components. It aims to be applied to real life situations (in particular in work environments) and we provide a typical example which naturally leads to a problem of control.

Estimating the State of Health of Lithium-Ion Batteries with a High Discharge Rate through Impedance

Lithium-ion batteries are an attractive power source in many scenarios. In some particular cases, including providing backup power for drones, frequency modulation, and powering electric tools, lithium-ion batteries are required to discharge at a high rate (2~20 C). In this work, we present a method to estimate the state of health (SOH) of lithium-ion batteries with a high discharge rate using the battery’s impedance at three characteristic frequencies. Firstly, a battery model is used to fit the impedance spectrum of twelve LiFePO4 batteries. Secondly, a basic estimation model is built to estimate the SOH of the batteries via the parameters of the battery model. The model is trained using the data of six batteries and is tested on another six. The RMS of relative error of the model is lower than 4.2% at 10 C and lower than 2.8% at 15 C, even when the low-frequency feature of the impedance spectrum is ignored. Thirdly, we adapt the basic model so that the SOH estimation can be performed only using the battery’s impedance at three characteristic frequencies without having to measure the entire impedance spectrum. The RMS of relative error of this adapted model at 10 C and 15 C is 3.11% and 4.25%, respectively.

Thermodynamic Effects on Pressure Fluctuations of a Liquid Oxygen Turbopump

Liquid oxygen turbopumps are an important component of rocket engines. The instability induced by cavitation flow in turbopumps has received considerable attention because of thermodynamic effects. In this study, unsteady numerical simulations of a turbopump with thermodynamic effects were performed. The frequency composition and source of pressure fluctuations in a turbopump were analyzed, and the difference in pressure fluctuations with/without thermodynamic effects was revealed. The results showed that the pressure fluctuations were mainly caused by the interaction between the impeller and diffuser, and the thermodynamic effects slightly increased the amplitudes of the characteristic frequencies. In addition, in the inducer and impeller, three characteristic frequencies (4.089fn, 2.519fn, and 3.238fn, where fn is the rotational frequency) were confirmed. Analyses revealed that the 4.089fn was due to the periodic shedding of cavitation structures on the suction surfaces at the inducer outlet, 2.519fn was induced by the periodic occurrence and collapse of cavitation on the suction surfaces at the impeller inlet; and 3.238fn was from the periodic shedding of cavitation structures on the suction surfaces at the impeller middle blades. The existence of thermodynamic effects decreased the frequency of cavitation shedding and increased the frequency of the periodic occurrence and collapse of cavitation.

Inaccessible rolling bearing diagnosis using a novel criterion for Morlet wavelet optimization

The objective of this research is to diagnose an inaccessible rolling bearing by indirect vibration measurement. In this study, a shaft supported with several bearings is considered. It is assumed that the vibration for at least one bearing is not recordable. The purpose is to diagnose inaccessible bearing by the recorded data from the sensors located on the other bearings. To achieve this goal, the continuous wavelet transform is used to detect weak signatures in the available vibration signals. A new criterion for adjusting the scale parameter of continuous wavelet transform is proposed based on the amplitude of the bearing characteristic frequencies. In this criterion, the optimal scale is selected to maximize the amplitude of bearing characteristic frequencies in comparison with the amplitude of the other frequencies. The results of the proposed method are compared with a popular method, energy-to-entropy ratio criterion, using two different sets of run-to-failure experimental data. Results indicate that the proposed method in this article is more effective and efficient for extracting the weak signatures and diagnosing inaccessible bearings from the recorded vibration signals.