Journal of Vibroengineering 

About: Journal of Vibroengineering is an academic journal published by JVE International. The journal publishes majorly in the area(s): Vibration & Finite element method. It has an ISSN identifier of 1392-8716. It is also open access. Over the lifetime, 2820 publications have been published receiving 12629 citations. The journal is also known as: JVE.

Invited Review: Recent developments in vibration control of building and bridge structures

Abstract: This paper presents a state-of-the-art review of recent articles published on active, passive, semi-active and hybrid vibration control systems for structures under dynamic loadings primarily since 2013. Active control systems include active mass dampers, active tuned mass dampers, distributed mass dampers, and active tendon control. Passive systems include tuned mass dampers (TMD), particle TMD, tuned liquid particle damper, tuned liquid column damper (TLCD), eddy-current TMD, tuned mass generator, tuned-inerter dampers, magnetic negative stiffness device, resetting passive stiffness damper, re-entering shape memory alloy damper, viscous wall dampers, viscoelastic dampers, and friction dampers. Semi-active systems include tuned liquid damper with floating roof, resettable variable stiffness TMD, variable friction dampers, semi-active TMD, magnetorheological dampers, leverage-type stiffness controllable mass damper, semi-active friction tendon. Hybrid systems include shape memory alloys-liquid column damper, shape memory alloy-based damper, and TMD-high damping rubber.

A rail-borne piezoelectric transducer for energy harvesting of railway vibration

Abstract: This paper investigates design, modelling, and test issues related to piezoelectric energy transducer. The model analyzes a rail-borne “seismic” energy harvester that is designed to generate electrical energy from local variations in rail acceleration. The energy harvester analyzed in this model consists of a piezoelectric PZT film clamped at one end to the rail with a tip mass mounted on its other end. It includes two sub-models in this paper: a vehicle-track interaction model considering vehicle travelling load; and a cantilevered piezoelectric beam model for the visualization of voltage and power profile and frequency response. Four rail irregularities (American 6th grade track spectrum, Chinese track spectrum, German high and low-disturbance track spectrum) are compared and implemented into the calculation script. The calculated results indicate a rail displacement of 0.2 mm to 0.8 mm. Vibration tests of the proposed rail-borne device are conducted; a hydraulic driven system with excitation force up to 140 kN is exploited to generate the realistic wheel-rail interaction force. The proposed rail-borne energy harvester is capable of energy harvesting at low-frequency (5 Hz to 7 Hz) and small railway vibration (0.2 mm to 0.4 mm rail displacement). The output power of 4.9 mW with a load impedance of 100 kOhm is achieved. The open circuit peak-peak voltage reaches 24.4 V at 0.2 mm/7 Hz/5 g wheel-rail excitation. A DC-DC buck converter is designed, which works at the resonance frequency of 23 Hz/5 g on a lab vibration rig, providing a 3.3 VDC output.

Multi-layer neural network with deep belief network for gearbox fault diagnosis

Abstract: Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based learning architecture using deep belief network (MLNNDBN) is proposed for gearbox fault diagnosis. Training process of the proposed learning architecture includes two stages: A deep belief network is constructed firstly, and then is trained; after a certain amount of epochs, the weights of deep belief network are used to initialize the weights of the constructed MLNN; at last, the trained MLNN is used as classifiers to classify gearbox faults. Multidimensional feature sets including time-domain, frequency-domain features are extracted to reveal gear health conditions. Experiments with different combined faults were conducted, and the vibration signals were captured under different loads and motor speeds. To confirm the superiority of MLNNDBN in fault classification, its performance is compared with other MLNN-based methods with different fine-tuning schemes and relevant vector machine. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.

Noise and environmental pollution from transport: decisive problems in developing ecologically efficient transport systems

Abstract: The paper discusses problems of sustainable development of transport systems with special attention paid to noise pollution and emission of harmful compounds of exhaust gases. It presents traffic flow model for a transport network, which can be applied for estimation of external costs of transport resulting from congestion and congestion-related effects like noise and harmful emission. Model includes structure of vehicle stock, segmentation of transport, characteristics of roads and elements of surroundings. It allows for computational experiments with traffic distribution into transport network and traffic management and, in consequence, estimation of environmental effects of traffic flow. Formal model was supported by numerical example for transport network of Mazowieckie voivodeship in Poland. The example is based on noise pollution and its external costs. The model was developed on the base of EMITRANSYS model for Mazowieckie in 2016.

Application of vibration signal in the diagnosis of IC engine valve clearance

Abstract: The article describes a concept of a non-invasive method for diagnosing the size of valve clearance in internal combustion engines, based on the analysis of engine surface vibration signals using artificial neural networks. The applicability of the method was tested on a single-cylinder compression-ignition engine with a low power rating, which had an OHV timing gear, acting indirectly on the valves, and manual adjustment of valve clearance. The method uses as diagnostic signals the readings of vibration sensors, which record the acceleration of engine head movement as a function of the angle of rotation of the crankshaft, with pre-set valve clearance values measured in a cold condition. From among the signals recorded, components corresponding to the impact of rocker arms against valve stems were identified and low-pass filtered in order to eliminate measurement interference. A classifier of selected features of the signals processed was constructed using artificial neural networks. This classifier recognizes signals generated by engines with correct valve clearance as well as those with too much and too little valve clearance.