Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

Ahmad, Zahoor; Nguyen, Tuan-Khai; Ahmad, Sajjad; Nguyen, Cong Dai; Kim, Jong‐Myon

doi:10.3390/s22010179

Cited by 25 publications

(26 citation statements)

References 46 publications

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“…Ahm al. [10] proposed a fault diagnosis method for multistage centrifugal pumps usin formative ratio principal component analysis. These studies mainly focus on the fe extraction of the collected state data, to achieve the purpose of classifying equipmen ures.…”

Section: Centrifugal Pump Operation Failure and Its Featuresmentioning

confidence: 99%

“…Additionally, the method was applied to diagnose incipient cavitation failures in a water supply network centrifugal pump. Ahmad et al [ 10 ] proposed a fault diagnosis method for multistage centrifugal pumps using informative ratio principal component analysis. These studies mainly focus on the feature extraction of the collected state data, to achieve the purpose of classifying equipment failures.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Chen

Wei

Wang

et al. 2022

Sensors

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Centrifugal pumps have a wide range of applications in industrial and municipal water affairs. During the use of centrifugal pumps, failures such as bearing wear, blade damage, impeller imbalance, shaft misalignment, cavitation, water hammer, etc., often occur. It is of great importance to use smart sensors and digital Internet of Things (IoT) systems to monitor the real-time operating status of pumps and predict potential failures for achieving predictive maintenance of pumps and improving the intelligence level of machine health management. Firstly, the common fault forms of centrifugal pumps and the characteristics of vibration signals when a fault occurs are introduced. Secondly, the centrifugal pump monitoring IoT system is designed. The system is mainly composed of wireless sensors, wired sensors, data collectors, and cloud servers. Then, the microelectromechanical system (MEMS) chip is used to design a wireless vibration temperature integrated sensor, a wired vibration temperature integrated sensor, and a data collector to monitor the running state of the pump. The designed wireless sensor communicates with the server through Narrow Band Internet of Things (NB-IoT). The output of the wired sensor is connected to the data collector, and the designed collector can communicate with the server through 4G communication. Through cloud-side collaboration, real-time monitoring of the running status of centrifugal pumps and intelligent diagnosis of centrifugal pump faults are realized. Finally, on-site testing and application verification of the system was conducted. The test results show that the designed sensors and sensor application system can make good use of the centrifugal pump failure mechanism to automatically diagnose equipment failures. Moreover, the diagnostic accuracy rate is above 85% by using the method of wired sensor and collector. As a low-cost and easy-to-implement solution, wireless sensors can also monitor gradual failures well. The research on the sensors and pump monitoring system provides feasible methods and an effective means for the application of centrifugal pump health management and predictive maintenance.

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Section: Centrifugal Pump Operation Failure and Its Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Chen

Wei

Wang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…With the help of such an optimization process, the vector lr can be smaller in order to improve the convergence speed of the training model and protect against the possibility of overfitting. At this stage, Equation ( 16) can be expressed as Equation (17) below:…”

Section: Adaptive Learning Approachmentioning

confidence: 99%

“…[14][15][16] In this scenario, numerous solutions have been explored to prevent performance degradation. 17 Recently, deep learning (DL) has turned the academic community's focus away from the aforementioned principles and has strong application potential to motivate end-to-end learning tasks to address the generalization gap. 18 The DL models, such as convolutional neural network (CNN), 19 recurrent neural network (RNN), 20 deep auto encoder (DAE), 21 and generative adversarial networks (GAN), 22 display ever-improving feature distillation capabilities.…”

Section: Introductionmentioning

confidence: 99%

Few-shot pump anomaly detection via Diff-WRN-based model-agnostic meta-learning strategy

Zou

Sang

Jiang

et al. 2022

Structural Health Monitoring

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As a critical component in agriculture, industry, and the military, pump anomaly detection has recently aroused wide attention, which requires deep and abundant development and application. Researchers emphasize deeper networks that are long vast computational resources despite insufficient training samples prepared. To break through this obstacle above, we propose a few-shot model-agnostic meta-learning strategy (MAMLS) model to mitigate the data scarcity problem. Inspired by the diffusive ordinary differential equations (ODEs) and Wide-Resnet (WRN), we made great strides by connecting diffusion (Diff) mechanism and self-adaptive Lr with MAMLS. We generate two classical synthetic datasets (circle and spiral) to clarify the diffusion algorithm’s capability to enhance the relationships and weaken the noise. The experimental results under synthetic data confirm that accuracy quickly reached 99% after several iterations. In an actual case anomaly detection study on the pumps simulation platform, the proposed Diff-WRN-MAMLS brings substantial advantages in saving hardware resources. Compared to current models, our model achieves 98% accuracy in 9-way 25-shot tasks. In the operating efficiency experiment, our algorithm only consumed 14.37 quality factors. The final experiment with four state-of-the-art model-agnostic meta-learning (MAML)-enhanced methods demonstrates the highest reliable test accuracy in different cases, reaching 98.5, 97.8, and 98.4%, respectively. Results showed that the proposed method will generalize surprisingly well in anomaly detection in future research.

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“…In summary, several internal and external monitoring methods have been developed to detect pipeline leaks. These include the use of negative pressure wave techniques [ 13 ], techniques based on accelerometer [ 14 ], time-domain reflectometry [ 15 ], distributed temperature sensing systems [ 16 ], acoustic emission technology [ 17 ], ultrasonic technology [ 18 ], and magnetic flux leakage techniques [ 19 ]. Among these, acoustic emission technology has gained significant popularity for its ability to quickly detect leaks, with real-time responses, high sensitivity, and ease of retrofit [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Ullah

Ahmad

Kim

2023

Sensors

Self Cite

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Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform.

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Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

Cited by 25 publications

References 46 publications

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Few-shot pump anomaly detection via Diff-WRN-based model-agnostic meta-learning strategy

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

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