A multi-sensor approach to remaining useful life estimation for a slurry pump

Tse, Yiu Lun; Cholette, Michael E.; Tse, Peter W.

doi:10.1016/j.measurement.2019.02.079

Cited by 33 publications

(12 citation statements)

References 21 publications

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“…A trend found within the data sets utilised is that many rely on vibration measurements [81][82][83][84]86,87]. Vibrations are common within pumps, but also generally applied.…”

Section: Pumps and Thermal Power Plantsmentioning

confidence: 99%

“…Furthermore, given a little pre-processing the degradation curve becomes fairly detectable. In a paper by Tse et al [87], a slurry pump is studied. Multiple vibration sensor data is extracted and fusioned.…”

Section: Pumps and Thermal Power Plantsmentioning

confidence: 99%

“…If vibration data is not available other methods have to be deployed. Furthermore, a component can have multiple sensors as a source for a model, which requires the developer to align and pre-process data [86,87]. Furthermore, industrial data sets, typically, do not have a labelling metric, hence it requires the developer to dive into expert knowledge or company ERP systems to identify faulty data, which in turn can introduce bias.…”

Section: Challenges For Predictive Maintenance Applicationsmentioning

confidence: 99%

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Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

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“…A trend found within the data sets utilised is that many rely on vibration measurements [81][82][83][84]86,87]. Vibrations are common within pumps, but also generally applied.…”

Section: Pumps and Thermal Power Plantsmentioning

confidence: 99%

Section: Pumps and Thermal Power Plantsmentioning

confidence: 99%

Section: Challenges For Predictive Maintenance Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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“…While going through the literature, it was found that only four researchers, i.e., [ 3 , 4 , 5 , 6 ], have estimated the RUL of slurry pumps. However, their work was deficient in terms of applicability since all of them used the curve fitting technique for RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Tse

Trappey

2021

Sensors

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Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.

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“…Concrete can be transported efficiently to the construction platform or formwork by pumping, which can speed the construction with low cost and high quality [ 4 , 5 ]. With the accumulation of construction experience and the improvement of pumping equipment [ 6 , 7 ], concrete pumping has been continuously improved to be a mainstream construction technology [ 8 , 9 ]. In order to ensure the smooth implementation of concrete pumping, it is necessary to evaluate the pumpability of concrete before pumping [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Different Measurement Methods for Characterizing Rheological Properties of Lubrication Layer

2021

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The lubrication layer plays a governing role in predicting the pumpability of fresh concrete. To determine the effect of measurement methods on the characterization of the rheological properties of the lubrication layer, different measurement systems, including Sliper, tribometer, and the utilization of a mortar rheometer, were employed. The rheological properties and workability of bulk concrete were measured in parallel to investigate the correlation between them and the rheological properties of the lubrication layer. The results show that the measured values of the rheological parameters of the lubrication layer differ due to the systematic deviation between different measurement methods. The results obtained by both tribometer and mortar rheometer were well-correlated, having a linear relationship with the rheological parameters of bulk concrete. The correlation coefficient between results gained with Sliper and rheological parameters of concrete or lubrication layer determined with other methods was not high enough. Addition friction led to the large accidental error and overestimated yield stress obtained with Sliper. The workability of concrete is only suitable for characterizing the rheological properties of bulk concrete.

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A multi-sensor approach to remaining useful life estimation for a slurry pump

Cited by 33 publications

References 21 publications

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

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

Comparative Study of Different Measurement Methods for Characterizing Rheological Properties of Lubrication Layer

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