Acoustic wayside identification of freight car roller bearing defects

Cline, J.E.; Bilodeau, Jean-Sébastien; Smith, Roger

doi:10.1109/rrcon.1998.668083

Cited by 39 publications

(30 citation statements)

References 1 publication

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“…Many methods which are based on ultrasonics [1][2][3][4], electromagnetic ultrasounds [5], acoustic wayside detectors [1,6,7], and "excessive" heat detection systems [1,8] have been presented for fault detection in wheels, axles and roller bearings.…”

Section: Introductionmentioning

confidence: 99%

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

2015

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The design of a vibration based fault detection and isolation (FDI) unit that can tackle the combined problem of fault detection, isolation (or identification) and magnitude estimation (collectively known as fault diagnosis), in railway vehicle suspensions is presented. The unit is initially "trained" in a baseline phase based on data obtained from a simplified physics-based model of a railway vehicle suspension. Fault diagnosis is subsequently achieved in an inspection phase through a single, properly preselected, pair of vibration signals acquired from the vehicle, and a recently introduced data-based method, referred to as the functional model based method (FMBM), without resorting to the physics-based model of the baseline phase. The method's cornerstone is the novel class of stochastic ARX-type models capable of accurately representing a system in a faulty state for its continuum of fault magnitudes. Fault diagnosis feasibility in a railway vehicle suspension is demonstrated via Monte Carlo simulations using different types and magnitudes of faults in the physics-based model and generating vibration signals corresponding to the healthy and faulty suspension. Two vibration signals are used by the diagnosis unit: the track velocity profile and the vehicle body acceleration above the trailing airspring. Fault diagnosis based on the FMBM is effective in a compact and unified statistical framework accounting for experimental and modelling uncertainty through appropriate interval estimates and hypothesis testing procedures. The unit is shown to exhibit high sensitivity and accurate estimation of even very small fault magnitudes, to detect and isolate unknown faults for which it has not been trained, and to be robust to high measurement noise, car body mass variations, and varying track irregularity.

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Section: Introductionmentioning

confidence: 99%

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

2015

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“…The bearing of freight train is rolling bearing (cylindrical bearing, conical bearing and self-aligning roller bearing), the monitoring parameters of bearing are noise obtained by ultrasonography monitoring or acoustic emission, temperature obtained by infrared or fiber monitoring, lubricating status obtained by infrared spectroscopy or ferrography, and vibration which is used much more widely. The monitoring method widely used for bearing of passenger train are on-board contact and rail-side induction detections, the monitoring equipments of rail-side induction detection include trace hotbox detection system (THDS) and trace acoustics detection system (TADS) [4,5]. But because of some intractable technical problems, high cost in long-distance monitoring and operating characteristics of freight train, the leak detection and high misdiagnosis rate with bearing fault of freight train could not be solved satisfactorily by on-board contact and rail-slide induction detections.…”

Section: Monitoring Methods To Bearing Of Freight Train Based On Wsnsmentioning

confidence: 99%

On-line Monitoring Method of Bearing in Rotating Machinery Based on Wireless Sensor Networks

Wang¹,

Liang²,

Wang³

2018

Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

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Abstract.For acquiring the precise parameters of bearing in rotating machinery, and further to study the bearing characteristics, and considering the difficult and bottleneck problems with wired monitoring method for bearing, the on-line monitoring method of bearing based on wireless sensor networks (WSNs) is proposed in this paper. Two typical engineering applications, namely the bearing of freight train and the water-lubricated bearing are studied. The key issues in application of WSNs, including the energy consumption, the routing protocol, the design of node, the compressive and coding algorithm of vibration data, and the Doppler effect are solved. Finally, the simulation and experiment results show the effective of the wireless method proposed.

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“…In the TADS, microphone arrays were placed on both tracksides, recording the acoustic signals when the train passes. This system can detect thousands of bearings in one day, and the signals are transmitted to the workstation or the server to diagnose the possible fault of train bearing using a complex algorithm [4]. In the existing TADS, there exist some technical difficulties that may cause wrong diagnostic results.…”

Section: Introductionmentioning

confidence: 99%

Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

et al. 2014

IEEE Trans. Instrum. Meas.

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Multiscale noise tuning stochastic resonance (MSTSR) has been proved to be an effective method for enhanced fault diagnosis by taking advantage of noise to detect the incipient faults of the bearings and gearbox. This paper addresses a sequential algorithm for the MSTSR method to detect the train bearing faults in an embedded system through the acoustic signal analysis. Specifically, the energy operator, digital filter array, and fourth rank Runge-Kutta equation methods are designed to realize the signal demodulation, multiscale noise tuning, and bistable stochastic resonance in sequence. The merit of the sequential algorithm is that it reduces the memory consumption and decreases the computation complexity, so that it can be efficiently implemented in the embedded system based on a low-cost, low-power hardware platform. After the sequential algorithm, the real-valued fast Fourier transform is used to calculate the power spectrum of the analyzed signal. The proposed method has been verified in algorithm performance and hardware implementation by three kinds of practical acoustic signals from defective train bearings. An enhanced performance of the proposed fault diagnosis method is confirmed as compared with several traditional methods, and the hardware performance is also validated.Index Terms-Embedded system, fault diagnosis, multiscale noise tuning, sequential algorithm, stochastic resonance (SR), train bearing.

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Acoustic wayside identification of freight car roller bearing defects

Cited by 39 publications

References 1 publication

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

On-line Monitoring Method of Bearing in Rotating Machinery Based on Wireless Sensor Networks

Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

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