Order tracking using the Vold–Kalman tracking filter

Gade, Svend; Herlufsen, H.; Konstantin-Hansen, H.

doi:10.1121/1.424909

Cited by 14 publications

(5 citation statements)

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“…In this paper, the order analysis technique has been used to calculate some characteristics frequency indicators that can be affected by the machine's speed. Order analysis (Gade et al, 1995) is a technique for analyzing noise and vibration signals in rotating or reciprocating machinery. Such machinery typically has a variety of mechanical parts such as a shaft, bearing, gearbox, fan blade, belt, etc., where each mechanical part generates unique noise and vibration patterns as the machine operates, with each part contributing a unique component to the overall machine noise and vibration.…”

Section: Figure 2 Data Collection Points On a Reciprocating Compressormentioning

confidence: 99%

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Wang¹,

Ngolah²,

Morden

2011

International Journal of Software Science and Computational Intelligence

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Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

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Section: Figure 2 Data Collection Points On a Reciprocating Compressormentioning

confidence: 99%

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Wang¹,

Ngolah²,

Morden

2011

International Journal of Software Science and Computational Intelligence

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“…Many studies have been conducted in which the problem of eliminating the impact of changes in rotational speed and load condition on the vibration signal was addressed [7][8][9]. One way to do this is to use synchronous methods [10][11][12], which allow for the elimination of spectrum blur resulting from variable rotational speed [5]. However, variable operating conditions also affect the values of spectral amplitudes [13,14].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine

Pawlik¹,

Kania²,

Przysucha³

2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The fault diagnosis for maintenance of machines operating in variable conditions requires special dedicated methods. Variable load or temperature conditions affect the vibration signal values. The article presents a new approach to diagnosing rotating machines using an artificial neural network, the training of which does not require data from the damaged machine. This is a new approach not previously found in the literature. Until now, neural networks have been used for machine diagnosis in the form of classifiers, where data from individual faults were required. A new diagnostic parameter rDPNS (Relative Differences Product of Network Statistics) as a function of the machine's shaft order was proposed as a kind of new order spectrum independent of the machine's operating conditions. The presented work analyses the use of the proposed method to diagnose misalignment and unbalance. The results of an experiment carried out in the laboratory demonstrated the effectiveness of the proposed method.

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“…The synchronous methods were known as early as in the 1980s, and their implementation involved synchronous signal sampling during the measurement [10] [11]. Currently, the synchronous methods involve the analysis of recorded signal and the application of such analytical methods as decimation [12] [13] DIAGNOSTYKA, Vol. 21, No.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the impact of oil viscosity on the vibroacoustic signal of a planetary gearbox operating at variable load

Pawlik¹

2020

Diagnostyka

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Order tracking using the Vold–Kalman tracking filter

Cited by 14 publications

References 0 publications

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine

Analysis of the impact of oil viscosity on the vibroacoustic signal of a planetary gearbox operating at variable load

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