Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor

Cabrera, Diego; Guamán, Adriana; Zhang, Shaohui; Cerrada, Mariela; Sánchez, René–Vinicio; Cevallos, J.P. Cobena; Long, Jianyu; Li, Chuan

doi:10.1016/j.neucom.2019.11.006

Cited by 103 publications

(59 citation statements)

References 34 publications

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“…Finally, Table 4 shows the performance of the proposed SD and CCM features over the dataset in [20].…”

Section: Resultsmentioning

confidence: 99%

“…This has motivated to set up a test-bed platform that enables investigation of a more extensive set of faults in valves as well as combined faults of valves and roller bearings. In the previous research [20], a deep learning approach based on long short-term memory (LSTM) models has been used for classifying 17 different faulty conditions in valves of a two-stage reciprocating compressor. The multi-fault dataset considers combinations of faults in the intake and discharge valves of the first and second stages.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes the use of Symbolic Dynamics (SD) and Complex Correlation Measure (CCM) as novel features for fault diagnosis in rotating machinery, as an alternative regarding complex computational architectures such as deep learning approaches. We used the same test-bed platform in [20] to investigate the classification of combined faults of valves and roller bearing by using vibration signals, also called "multi-fault scenario". The dataset of combined faults of valves and roller bearings considers the same set of four basic fault conditions for the discharge valve of the second stage of the compressor, in combination with three common faults of the roller bearings.…”

Section: Introductionmentioning

confidence: 99%

“…The dataset of combined faults of valves and roller bearings considers the same set of four basic fault conditions for the discharge valve of the second stage of the compressor, in combination with three common faults of the roller bearings. Additionally, we also test the proposed features with the valve faults dataset in [20] and perform the comparison to that previous research, in order to highlight the proper classification accuracy regarding the use of deep learning architectures. The original contributions of this paper are the following:…”

Section: Introductionmentioning

confidence: 99%

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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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“…Finally, Table 4 shows the performance of the proposed SD and CCM features over the dataset in [20].…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

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“…In Figure 1, the LSTM hidden layer cell structure is presented, which can be made up of memory cell, input gate, output gate and forget gate. Among them, the memory cell remembers the activation value over any time intervals, and three gates regulate the input and output of information flow to the unit [33]. The update functions are formulated as follows:…”

Section: Lstm Theorymentioning

confidence: 99%

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Huang

et al. 2020

Sensors

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Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.

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Using Kullback-Leibler Divergence to Identify Prominent Sensor Data for Fault Diagnosis

Monteiro

Bastos-Filho

2020

Lecture Notes in Computer Science

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Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor

Cited by 103 publications

References 34 publications

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

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

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Using Kullback-Leibler Divergence to Identify Prominent Sensor Data for Fault Diagnosis

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