Diesel Injector Dynamic Modelling and Estimation of Injection Parameters from Impact Response Part 2: Prediction of Injection Parameters from Monitored Vibration

Gu, Fengshou; Ball, Andrew; Rao, K. K.

doi:10.1243/pime_proc_1996_210_277_02

Cited by 15 publications

(6 citation statements)

References 2 publications

(1 reference statement)

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“…used a Cohen-Posch distribution, G u et al [2] used a Choi-Williams distribution and G u et al [3] used a Wigner-Ville distribution. In addition, timescale data processing has also been widely employed.…”

mentioning

confidence: 99%

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Webber

Payne

et al. 2003

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper (Part 1) describes the principles of a novel unsupervised adaptive neural network anomaly detection technique, called componential coding, in the context of condition monitoring of electrical machines. N umerical examples are given to illustrate the technique' s capabilities. The companion paper (Part 2), which follows, assesses componential coding in its application to real data recorded from a known machine and an entirely unseen machine (a conventional induction motor and a novel transverse ux motor respectively). Componential coding is particularly suited to applications in which no machine-speci c tailored techniques have been developed or in which no previous monitoring experience is available. This is because componential coding is an unsupervised technique that derives the features of the data during training, and so requires neither labelling of known faults nor pre-processing to enhance known fault characteristics. Componential coding offers advantages over more familiar unsupervised data processing techniques such as principal component analysis. In addition, componential coding may be implemented in a computationally ef cient manner by exploiting the periodic convolution theorem. Periodic convolution also gives the algorithm the advantage of time invariance; i.e. it will work equally well even if the input data signal is offset by arbitrary displacements in time. This means that there is no need to synchronize the input data signal with respect to reference points or to determine the absolute angular position of a rotating part.

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mentioning

confidence: 99%

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Webber

Payne

et al. 2003

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…This has limitations in nonstationary signals because transient events are lost in the averaging. Time-frequency representations permit the simultaneous representation of a signal's time, frequency and amplitude on a per cycle or per revolution basis [36].…”

Section: Advanced Signal-processing Algorithms In An Intelligent Sensormentioning

confidence: 99%

Systems integration in maintenance engineering

Starr

Ball

2000

Proceedings of the Institution of Mechanical Engineers, Part E:

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Integration in maintenance engineering systems provides a potential solution to some complex and conflicting problems. Decision making is often achieved with uncertainty and unknowns, while measuring against conflicting performance criteria. Maintenance decisions are made in the context of business priorities.Integration must consider the bidirectional flow of data and information into the decision-making and planning process at all levels. This reaches from business systems right down to sensor level. Integration automates the organization and operation of systems, data collection and actuation, information storage and decision making. Critically, integrated systems display the evidence of thorough top-down system design, which incorporates the elements above and closes the loop from the minutiae of data collection to strategic decision making.This paper introduces the systems associated with maintenance and draws comparisons with adjacent disciplines such as control. A globalized model is proposed, and methods for selecting and adapting technologies for the model are discussed. Examples of industrial implementation are shown at several levels within the model.

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“…Injector dynamics, needle movements and their resulting vibrations were studied by Gu and Ball [5,6]. They described the vibration characteristics of injectors by three series of transients during an injection cycle; fluid excitation commencing prior to needle impact, needle opening impact and needle closing impact.…”

Section: Introductionmentioning

confidence: 99%

Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

2010

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Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise.This work firstly investigates diesel engine air-borne acoustic signals characteristics and the benefits of joint time-frequency domain analysis. Secondly, the air-borne acoustic signals in the vicinity of injector head were recorded using three microphones around the fuel injector (120° apart from each other) and an Independent Component Analysis (ICA) based scheme was developed to decompose these acoustic signals. The fuel injection process characteristics were thus revealed in the timefrequency domain using Wigner-Ville distribution (WVD) technique. Consequently the energy levels around the injection process period between 11 and 5 degrees before the top dead center and of frequency band 9 to 15 kHz are calculated. The developed technique was validated by simulated signals and empirical measurements at different injection pressure levels from 250 to 210 bars in steps of 10 bars. The recovered energy levels in the tested conditions were found to be affected by the injector pressure settings.

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Diesel Injector Dynamic Modelling and Estimation of Injection Parameters from Impact Response Part 2: Prediction of Injection Parameters from Monitored Vibration

Cited by 15 publications

References 2 publications

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Systems integration in maintenance engineering

Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

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