Engine Fault Analysis: Part II---Parameter Estimation Approach

Sood, Arun K.; Fahs, Ali Amin; Henein, Naeim A.

doi:10.1109/tie.1985.350101

Cited by 42 publications

(7 citation statements)

References 7 publications

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“…When the engine is operating in a steady state condition and the crankshaft system is modelled as rigid, the engine crankshaft motion is described by the torque balance equation [5,8,10,11]:…”

Section: Cylinder Pressure and Crankshaft Speedmentioning

confidence: 99%

“…This torque does not contribute net kinetic energy to the system but creates significant fluctuation in the resultant torque at the combustion frequency; it is determined by the engine geometry and speed. For the non-linear engine model [10,11], this torque is expressed as a function of the instantaneous crankshaft speed and angular momentum:…”

Section: Cylinder Pressure and Crankshaft Speedmentioning

confidence: 99%

“…In the work reported in references [10], [11] and [14], the friction and pump torques were estimated using the engine speed and geometry. Ribbens [15] argued that crankshaft acceleration measurements contain information on both the average and time-varying engine torque applied to the crankshaft.…”

Section: Cylinder Pressure and Crankshaft Speedmentioning

confidence: 99%

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Non-parametric models in the monitoring of engine performance and condition: Part 1: Modelling of non-linear engine processes

Jacob

Ball

1999

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper proposes the use of radial basis function (RBF) networks in the modelling of non-linear engine processes. A pertinent application of such a model is the reconstruction of cylinder pressure based upon the instantaneous angular velocity of the engine crankshaft. Distinction is made between parametric and non-parametric models and applications to which each is suited. The structure of an RBF model is presented and the use of this model in combustion pressure reconstruction is discussed. The paper concludes with a treatment of the practicalities associated with the implementation of an RBF model to typify a non-linear engine process.Keywords: angular speed, cylinder pressure measurement, non-parametric engine model, radial basis function network, regressor selection, regularization NON-LINEAR ENGINE MODELSMany of the systems and processes operating within internal combustion engines are inherently non-linear; because of this, simple but accurate analytical models cannot be derived for them. A classic example of a non-linear engine process is the cyclic pressure variation within a combusting diesel engine cylinder. Owing to its practical importance, cylinder pressure estimation has been used as the process upon which to develop the non-linear modelling techniques illustrated in this paper. Combustion pressure waveform measurement and analysis play an important role in the improvement of performance, emissions control and condition monitoring in internal combustion engines. Conventional techniques are inapplicable for reliable, high-resolution cylinder pressure measurement on a routine or in-service basis. For these reasons an alternative approach to cylinder pressure estimation, based on easy-to-measure variables, is attractive. This paper introduces a family of non-parametric models, radial basis function (RBF) networks, which may be applied to the task of reconstructing cylinder pressure based on easy-to-obtain measurements of instantaneous crankshaft angular velocity and cylinder head vibration.At this stage it is worthwhile formally distinguishing between parametric and non-parametric models. Parametric models are differentiated from non-parametric models by the inference which may be drawn from their coefficients. In the case of the former, the aim is to arrive at values for a select few terms that are meaningful in the real world. It is assumed that a set of deterministic mathematical models of the system can be derived using physical laws. The system can then usually be written as a set of ordinary differential equations and algebraic equations: y= f (q, q; , q,x,t,) (where q represents the degrees of freedom, y the observed output variables, x the input variables and t the explicit time. Vector represents the unknown model parameters. Sufficient initial conditions must be supplied to arrive at values for q and its first and second derivatives. In order to determine the model parameters, , it is necessary to substitute the measured time histories of all variables. This assumes that the m...

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Section: Cylinder Pressure and Crankshaft Speedmentioning

confidence: 99%

Section: Cylinder Pressure and Crankshaft Speedmentioning

confidence: 99%

See 1 more Smart Citation

Non-parametric models in the monitoring of engine performance and condition: Part 1: Modelling of non-linear engine processes

Jacob

Ball

1999

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…25–27 Further, estimating the instantaneous indicated torque by deriving a mathematical model of engine dynamics is an efficient approach for misfire detection. 28–30 Research was conducted using the engine model information, to calculate the relative changes in kinetic energy of the pistons and the connecting rods for evaluation of the combustion. 31 For the physical model-based methods, an exact engine model is important for acquiring accurate estimation parameters, and the model parameters are time-varying and inaccessible in the actual working conditions of the engine.…”

Section: Introductionmentioning

confidence: 99%

Misfire detection of diesel engine based on convolutional neural networks

Zhang

Gao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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With the ever-stringent vehicles exhaust emission standard and higher requirements on on-board diagnostic technology, the importance of misfire detection in vehicle emission control is emerging. The performance of a traditional misfire detection algorithm predominantly depends on the features and classifier selected. Fixed and handcrafted features require either a reliable dynamic model of an engine or a large number of experiment data to define the threshold, and then, form a map. Since convolutional neural networks (CNNs) have an inherent adaptive design and integrate the feature extraction with classification functions into a compact learning framework, the misfire fault-sensitive features can be auto-discovered from raw speed signals. Furthermore, CNNs can detect the fault features of the misfire through network training with fewer engine operating conditions. In this paper, the theory and method of the misfire diagnosis based on CNNs are presented. The experimental data for network training and testing are sampled on a six-cylinder inline diesel engine. The misfire patterns containing every one-cylinder and two-cylinder misfire are tested under the wide speed and load conditions of the engine. The results show that when the engine operates under steady-state conditions, one-cylinder or two-cylinder complete misfires can be detected accurately by CNNs. In addition, one-cylinder partial misfire is employed to examine the adaptability of trained 1-D CNN. It turns out that when the partial misfire reaches the same level as half amount of the normal fuel injection quantity, one-cylinder partial misfire can be detected with accuracy more than 96%. At last, the misfire detection under the non-stationary conditions, such as acceleration or deceleration, is conducted. The results show the 1-D CNN performed well in a limited acceleration range, and network failure occurs when the absolute acceleration of the engine speed is more than 100 r/min/s.

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“…Moreover on-line fault diagnosis technology of incipient faults for induction drives is rapidly emerging to avoid the unpredictable failure [1]. Different invasive and non invasive approaches for motor incipient fault detection/diagnosis have been reported [11][12][13][14][15]. Many of the motor incipient fault detection/diagnosis schemes can be applied non invasively on-line without the need of expensive monitoring equipment by using a microprocessor.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Speed Sensor Failure in Induction Motor Drive

Zidani

Benbouzid

Berthelot

2007

2007 IEEE International Electric Machines &Amp; Drives Conference

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A large number of adjustable-speed drives in industry and emerging applications such as automotive (EV or HEV) require high dynamic performances, robustness against parameter variation and also reliability. parameter detuning and mechanical speed sensor faults lead to a deterioration of the performances and even to instability. therefore condition monitoring is becoming mandatory in those sensitive applications. the objective of this contribution is to study the feasibility of detection and diagnosis of the mechanical speed sensor faults in an induction motor drive. using knowledge of the motor condition, the proposed technique based on fuzzy logic is applied to discriminate load and parameter variations from the speed sensor faults. both simulation and experimental results are presented in terms of accuracy in the detection of speed sensor faults and knowledge extraction feasibility.

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Engine Fault Analysis: Part II---Parameter Estimation Approach

Cited by 42 publications

References 7 publications

Non-parametric models in the monitoring of engine performance and condition: Part 1: Modelling of non-linear engine processes

Non-parametric models in the monitoring of engine performance and condition: Part 1: Modelling of non-linear engine processes

Misfire detection of diesel engine based on convolutional neural networks

Diagnosis of Speed Sensor Failure in Induction Motor Drive

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