Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

Chen, Lei; Han, Jie; Lei, Wenping; Guan, Zhenhong; Gao, Yajuan

doi:10.1155/2017/6103947

Cited by 4 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These failure forms will cause changes in the pump vibration. Therefore, a sensor and IoT system [16][17][18][19][20] can be designed to form a digital unit and an industrial internet platform for smart pumps, which can effectively perform signal acquisition, analysis, feature extraction, and fault diagnosis, and realize the digital upgrading of pump equipment and the intelligent operation and maintenance of pump units [21,22]. The bearing is an important part of the centrifugal pump that supports the rotor.…”

Section: Centrifugal Pump Operation Failure and Its Featuresmentioning

confidence: 99%

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Chen

Wei

Wang

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Centrifugal Pump Operation Failure and Its Featuresmentioning

confidence: 99%

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Chen

Wei

Wang

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Multi-sensor data fusion methodologies include the holospectrum [24][25][26][27], full spectrum [28][29][30][31], and full vector spectrum [32]. Proposed by Han [32], the full vector spectrum has been widely studied and applied in engineering [33][34][35] and has formed the basis of many compound methods. Chen [36] applied full-vector signal acquisition and information fusion to fault prediction.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

Guan

Liu

et al. 2022

Shock and Vibration

View full text Add to dashboard Cite

The fault vibration signal of a bearing has nonstationary and nonlinear characteristics and can be regarded as the combination of multiple amplitude- and frequency-modulation components. The envelope of a single component contains the fault characteristics of a bearing. Local characteristic-scale decomposition (LCD) can decompose the vibration signal into a series of multiple intrinsic scale components. Some components can clearly reflect the running state of a bearing, and fault diagnosis is conducted according to the envelope spectrum. However, the conventional LCD takes a single-channel signal as the research object, which cannot fully reflect the characteristic information of the rotor, and the analysis results based on different channel signals of the same section will be inconsistent. To solve this problem, based on full vector spectrum technology, the homologous dual-channel information is fused. A vector LCD method based on cross-correlation coefficient component selection is given, and a simulation analysis is completed. The effectiveness of the proposed method is verified by simulated signals and experimental signals of a bearing, which provides a method for bearing feature extraction and fault diagnosis.

show abstract

“…The paper following will discuss the full vector spectrum technology [18], which is developed and proposed based on the holospectrum and the full spectrum. This technology defines the length of the semi-major axis of the rotating precessing elliptical orbit as the main vibration vector to evaluate the maximum vibration intensity of the rotor [18][19][20]. Although these methods have been applied to rotating machinery resoundingly, the averaging effect of the Fourier transform allows the method to process only stationary signals.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm

Cui,

Huang,

et al. 2021

Preprint

View full text Add to dashboard Cite

Full spectrum and holospectrum are homogenous information fusion technology developed for the fault diagnosis of rotating machinery and are often used in the analysis of the orbit of rotating machinery. However, both of the techniques are based on Fourier transform, so they can only handle stationary signals, which limits their development. By drawing inspiration from the approach of multivariate variational mode decomposition (MVMD) and the complex-valued signal decomposition, we propose a method called multivariate complex variational mode decomposition (MCVMD) for processing non-stationary complex-valued signals of multi-dimensional bearing surfaces in this work. In particular, the proposed method takes the advantages of the joint information between the complex-valued signals of multi-dimensional bearing surfaces, and owing to this property, we provide its three-dimensional instantaneous orbit map (3D-IOM) to present the overall perspective of the rotor-bearing system and also offer a high-resolution time-full spectrum (Time-FS) to display the forward and backward frequency components of all the bearing surfaces within a timefrequency plane. The effectiveness of the proposed method through both the simulated experiment and the real-life complex-valued signals are shown in this paper.

show abstract

Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

Cited by 4 publications

References 21 publications

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm

Contact Info

Product

Resources

About