Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner

Kim, Minsung; Yoon, Seok Ho; Domanski, Piotr A.; Payne, William V.

doi:10.1016/j.ijrefrig.2007.11.008

Cited by 122 publications

(50 citation statements)

References 10 publications

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“…does not vary with time [7]. Hence, if the data is at or very close to steady-state, then there is negligible uncertainty due to transients in any subsequent calculation.…”

Section: Methodsmentioning

confidence: 96%

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Estimating uncertainty when using transient data in steady-state calculations

Buswell

2016

Measurement

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When using measurement data for monitoring there is often a desire for steady-state analysis. On-line condition monitoring and fault detection systems are typical applications where the traditional way of treating transient data is to remove it using methods that require tuning using thresholds. This paper suggests an alternative approach where the uncertainty estimate in a particular variable is increased in response to the presence of transients and through propagation, varies the uncertainty in the result accordingly. The formulation of the approach is described and applied to two examples from building HVAC systems. The approach is demonstrated to be a pragmatic tool that can be used to increase the robustness of calculations from time series data.

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“…does not vary with time [7]. Hence, if the data is at or very close to steady-state, then there is negligible uncertainty due to transients in any subsequent calculation.…”

Section: Methodsmentioning

confidence: 96%

“…This has been the focus of research over the last 15-20 years, the aim of which is to help to manage complex systems in some automated sense using data [4,7,16,9,15,11].…”

Section: Introductionmentioning

confidence: 99%

Estimating uncertainty when using transient data in steady-state calculations

Buswell

2016

Measurement

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“…The sample rate of the data set, the n s and the S are parameters which have to be tuned for a desired function of a steady-state identification algorithm regarding to the application. Kim et al [9] studied this topic and presented results from an online steady-state detection application to a residential air conditioner. At each step τ the data set window moves t = t + S and the standard deviation of the included data points in the window, σ i,τ of variable i is calculated from:…”

Section: A Steady-state Detection Algorithmmentioning

confidence: 99%

Preprocessing of raw data for developing steady-state data-driven models for optimizing compressor stations

Xenos

Thornhill

Cicciotti

et al. 2014

2014 UKACC International Conference on Control (CONTROL)

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Abstract-Compressors operate in parallel to increase the supply of a gas in many applications in process industries (e.g. an air separation process). To optimally distribute the load of the compressors in parallel, an optimization problem is formulated that takes into account the operational constraints of the compressors and the objective is to reduce operational costs, i.e. power consumption of the drivers. The optimization takes place when the system is in steady-state. The structure of the optimization employs steady-state data-driven models to represent the operation in steady-state. Many researchers reported that the identification of steady-states of the data plays a key role for accurate representation of the actual process by a data-driven model. However, to the best of the authors' knowledge, there is not much research on the quantification of the influence of the output of the steady-state detection methods on the data-driven models. For these reasons there is a need to examine this topic.

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“…Bhat and Saraf [13] also expanded the F-like test to consider the early determination of steady states and gross error detection in a crude distillation unit by means of tuning the critical values, implementing a linear Kalman filter and performing data reconciliation by least-squares techniques. Kim et al [14] used a moving window of data and its standard deviations for the determination of steady states in a vapor compression system. Kelly and Hedengren [15] developed a method for SSI, which depends on the tuning of the window size and the critical value of its index, to detect non-stationary drifts in chemical processes.…”

Section: Introductionmentioning

confidence: 99%

A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets

2015

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This paper introduces a novel steady-state identification (SSI) method based on the auto-regressive model with exogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identifiability properties of the system. In particular, the singularity of the model matrices is used as an index for steady-state determination. In this contribution, the novel SSI method is compared to other available techniques, namely the F-like test, wavelet transform and a polynomial-based approach. These methods are implemented for SSI of three different case studies. In the first case, a simulated dataset is used for calibrating the output-based SSI methods. The second case corresponds to a literature nonlinear continuous stirred-tank reactor (CSTR) example running at different steady states in which the ARX-based approach is tuned with the available input-output data. Finally, an industrial case with real data of a depropanizer column from PETROBRAS S.A. considering different pieces of equipment is analyzed. The results for a reflux drum case indicate that the wavelet and the F-like test can satisfactorily detect the steady-state periods after careful tuning and when respecting their hypothesis, i.e., smooth data for the wavelet method and the presence of variance in the data for the F-like test. Through a heat exchanger case with different measurement frequencies, we demonstrate the advantages of using the ARX-based method over the other techniques, which include the aspect of online implementation.

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Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner

Cited by 122 publications

References 10 publications

Estimating uncertainty when using transient data in steady-state calculations

Estimating uncertainty when using transient data in steady-state calculations

Preprocessing of raw data for developing steady-state data-driven models for optimizing compressor stations

A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets

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