A Data-Driven Approach for Condition Monitoring of Reciprocating Compressor Valves

Guerra, Christopher J.; Kolodziej, Jason R.

doi:10.1115/1.4025944

Cited by 20 publications

(7 citation statements)

References 7 publications

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“…Guerra and Kolodziej [41] proposed a data-driven approach for condition monitoring of RC valves. An FFT was applied to the pressure wave measured in the environment around the discharge valve, and then the FFT values were grouped into several frequency bins.…”

Section: Fault Diagnosis Based On Pressures Measured In Other Volumesmentioning

confidence: 99%

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Liu

et al. 2021

Processes

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Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed.

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Section: Fault Diagnosis Based On Pressures Measured In Other Volumesmentioning

confidence: 99%

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Liu

et al. 2021

Processes

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“…In 2013, using the compressor in this work at the RIT Compression Test Cell, (Guerra & Kolodziej, 2014) developed a mechanical-thermodynamic model of the compressor and investigated health monitoring of discharge valves using P-V diagrams, dynamic pressure measurements, and frequency domain analysis. Later, (Kolodziej & Trout, 2018) extended this work into the time-frequency domain using image processing methods, and most recently (Scott & Kolodziej, 2020) investigated fault isolation of valve health across all manifolds, inlet and outlet, using a novelty detection SVM.…”

Section: Introductionmentioning

confidence: 99%

application based comparison of statistical versus deep learning approaches to reciprocating compressor valve condition monitoring

Chesnes

Kolodziej

2021

PHM_CONF

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This paper presents a vibration-based condition monitoring approach for early assessment of valve wear in an industrial reciprocating compressor. Valve seat wear is a common fault mode that is caused by repeated impact and accelerated by chatter. Seeded faults consistent with valve seat wear are installed on the head-side discharge valves of a Dresser-Rand ESH-1 industrial reciprocating compressor. Due to the cyclostationary nature of these units a time-frequency analysis is employed where targeted crank angle positions can isolate externally mounted, non-invasive, vibration measurements. A region-of-interest (ROI) is then extracted from the time-frequency analysis and used to train a suitably sized convolutional neural network (CNN). The proposed deep learning method is then compared against a similarly trained discriminant classifier using the same ROIs where features are extracted using texture and shape image statistics. Both methods achieve > 90% success with the CNN classification strategy nearing a perfect result.

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“…Several research works applying PHM technologies in reciprocating compressors have been reported. A research concerning the condition monitoring of the valves by using analytical and data-driven technologies is reported in [10]. The research analyzes different valve failure modes, and also investigates the type of sensors useful for detecting such faults.…”

Section: Introductionmentioning

confidence: 99%

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

et al. 2020

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Prognostics and Health Management technologies are useful for early fault detection and optimization of reliability in mechanical systems. Reciprocating compressors units are commonly used in industry for gas pressurization and transportation, and the valves in compressors are considered vulnerable parts susceptible to failure. Then, early detection of faults is important for avoiding catastrophic accidents. A feasible approach for fault detection consists in measuring the vibration signal for extracting useful features enabling fault detection and classification. In this research, a test-bed composed by two-stage reciprocating compressor was used for simulating a set of 13 different conditions of combined faults in valves and roller bearings. Three accelerometers were used for collecting the vibration signals for extracting three different types of features. These features were analyzed furthermore by using two random forest models to classifying the different faults. The first set of features was obtained by applying the symbolic dynamics algorithm, which provides the histogram of a set of symbols. This set of symbols was obtained by subdividing a 2D Poincaré plot into angular regions and counting the intersection of the phase trajectories on each of regions. The second type of features corresponds to the complex correlation measure which is calculated as the addition of the areas of triangles belonging to a Poincaré plot. Additionally, a small set of classical statistical features was also used for comparing their classification abilities to the new set of proposed features. The three sets of features enable highly accurate classification of the set of faults when used with random forest classification models. Notably, the ensemble subspace k-Nearest Neighbors algorithm provides classification accuracies higher than 99%.

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A Data-Driven Approach for Condition Monitoring of Reciprocating Compressor Valves

Cited by 20 publications

References 7 publications

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

application based comparison of statistical versus deep learning approaches to reciprocating compressor valve condition monitoring

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

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