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When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®. This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig.

Dynamic signals acquired from rotary machines can be characterized by the order tracking (OT) techniques. The extracted order components correspond to the operation of specific machine elements and reflect their current healthy or faulty states. The study extends the angular-velocity Vold–Kalman OT scheme to simultaneously extract multiple order components. Theoretical derivation is illustrated with simulation of processing three synthetic signals to show its merit. Additionally, as an example to validate its effectiveness, the improved OT scheme is used to process pass-by noise emitted from an electric scooter with a planetary-gear-set transmission system. The gear-meshing orders are effectively decoupled from structure-borne resonances.

In this paper we propose and implement an order tracking (OT) technique evaluating in an adaptive manner for signal interpretation and diagnosis of rotary machinery during operation. The paper comprises theoretical derivation, numerical implementation and experimental justification. Comparisons of the adaptive Vold-Kalman filtering OT (VKFOT) scheme to its conventional form are made through synthetic signal processing. Parameters such as the weighting factor, Q1 parameter, sampling frequency and process noise, which influence tracking performance, are investigated. Moreover, this adaptive OT scheme can be computed on-line and implemented as a real-time processing application.Copyright © 2009 by ASME

Dynamic signals acquired from rotary machines can be characterized by the order tracking (OT) techniques. The extracted order components correspond to the operation of specific machine elements, and reflect their current healthy or faulty states. The study extends the angular-velocity Vold-Kalman OT scheme to simultaneously extract multiple order components. Theoretical derivation is illustrated with simulation of processing two synthetic signals to show its merit. Additionally, as an example to validate the effectiveness, the developed OT scheme is used to rank pass-by noise emitted from an electric scooter with CVT transmission system.