Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data

Khan, Muhammad Mohsin; Tse, Peter W.; Trappey, Amy J.C.

doi:10.3390/s21248420

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The RUL prediction results obtained by the developed model were also compared with other existing methods. These comparative methods are typically used for online RUL prediction in the absence of run-to-failure data, as discussed in [13,17,20]. The obtained RUL prediction results comparison is shown in Tables 3 and 4.…”

Section: Resultsmentioning

confidence: 99%

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

2022

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An efficient Remaining Useful Life (RUL) prediction method is one of the most important features of a condition-based maintenance system. A running machine’s RUL prognosis in its real-time is a challenging task, especially when there is no historic failure data available for that particular machine. In this paper, an online RUL of an in-operation industrial slurry pump having no historical failure data has been predicted. At first, the available raw vibration datasets were filtered out for valid datasets. The obtained valid datasets were utilized for constructing the Health Degradation Trends (HDTs) using principal component analysis and a moving average method. Then, a novel procedure for automatically selecting the HDT’s data points for initiating the iteration process of prediction was formulated. Afterward, a hybrid deep LSTM model embedded with a smart learning rate mechanism was developed for estimating the online RUL using the selected points of HDTs. The online RUL prediction results produced by the developed model were quite satisfactory when they were compared with other online RUL prediction methods.

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Section: Resultsmentioning

confidence: 99%

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

2022

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“…Intelligent prognosis methods for remaining life in the condition-based maintenance of machinery are focused popularly nowadays. Khan et al [ 3 ] developed a novel method to predict the remaining life of the industrial slurry pump, especially for solving the existing challenge in the ideal database, which is the data acquired from the start of running to the final failure of the machinery. A hybrid nonlinear autoregressive model was developed to utilize the prior obtained vibration signal from slurry pumps to generate degradation trends.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis

Chen

2022

Sensors

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A new method of multi-sensor signal analysis for fault diagnosis of centrifugal pump based on parallel factor analysis (PARAFAC) and support vector machine (SVM) is proposed. The single-channel vibration signal is analyzed by Continuous Wavelet Transform (CWT) to construct the time–frequency representation. The multiple time–frequency data are used to construct the three-dimension data matrix. The 3-level PARAFAC method is proposed to decompose the data matrix to obtain the six features, which are the time domain signal (mode 3) and frequency domain signal (mode 2) of each level within the three-level PARAFAC. The eighteen features from three direction vibration signals are used to test the data processing capability of the algorithm models by the comparison among the CWT-PARAFAC-IPSO-SVM, WPA-PSO-SVM, WPA-IPSO-SVM, and CWT-PARAFAC-PSO-SVM. The results show that the multi-channel three-level data decomposition with PARAFAC has better performance than WPT. The improved particle swarm optimization (IPSO) has a great improvement in the complexity of the optimization structure and running time compared to the conventional particle swarm optimization (PSO.) It verifies that the proposed CWT-PARAFAC-IPSO-SVM is the most optimal hybrid algorithm. Further, it is characteristic of its robust and reliable superiority to process the multiple sources of big data in continuous condition monitoring in the large-scale mechanical system.

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“…Therefore, there is also a need to use feature extraction methods in the aforementioned features to improve the sensitivity to the degradation state of the device. Muhammad Mohsin Khan et al [ 4 ] utilized the obtained vibration signals from slurry pumps for generating degradation trends. Fernando Sánchez Lasheras et al [ 5 ] combined the multivariate adaptive regression splines technique with the principal component analysis (PCA), dendrograms, and classification and regression trees to extract elements from sensor signals and train a hybrid model.…”

Section: Introductionmentioning

confidence: 99%

A DLSTM-Network-Based Approach for Mechanical Remaining Useful Life Prediction

Liu

Zuo

et al. 2022

Sensors

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Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these methods still have the following weaknesses: (1) The correlation between the information collected by each sensor and the remaining useful life of the machinery is not sufficiently considered. (2) The accuracy of deep learning algorithms for remaining useful life prediction is low due to the high noise, over-dimensionality, and non-linear signals generated during the operation of complex systems. To overcome the above weaknesses, a general deep long short memory network-based approach for mechanical remaining useful life prediction is proposed in this paper. Firstly, a two-step maximum information coefficient method was built to calculate the correlation between the sensor data and the remaining useful life. Secondly, the kernel principal component analysis with a simple moving average method was designed to eliminate noise, reduce dimensionality, and extract nonlinear features. Finally, a deep long short memory network-based deep learning method is presented to predict remaining useful life. The efficiency of the proposed method for remaining useful life prediction of a nonlinear degradation process is demonstrated by a test case of NASA’s commercial modular aero-propulsion system simulation data. The experimental results also show that the proposed method has better prediction accuracy than other state-of-the-art methods.

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Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data

Cited by 6 publications

References 15 publications

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis

A DLSTM-Network-Based Approach for Mechanical Remaining Useful Life Prediction

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