Experimental set-up for investigation of fault diagnosis of a centrifugal pump

Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated

A new method of vibration analysis for the diagnosis of impeller in a centrifugal pump

Centrifugal pumps are widely used in many important industries such as power generation plants, chemical processes and petroleum refiners. The condition monitoring of centrifugal pumps is highly regarded by many researchers and users to minimize unexpected break-downs. Impellers are the core parts of pumps but often appear early damages due to flow cav-itation and erosion. This paper investigates a new approach to monitoring the conditions of impellers using surface vibration with advanced signal analysis. As overall vibration respons-es contain high level of broadband noises due to cavities and turbulences, noise reduction is critical to develop reliable and effective features. However, considering the modulation effect between rotating shaft and blade passing components, a modulation signal bispectrum (MSB) method is employed to extract these deterministic characteristics of modulations, which is different from previous researches in that broadband random sources are often used. Experi-mental results show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions

A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning.

Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagnosis framework for centrifugal pump that combines a signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model (ADCNN). First, the vibration signals corresponding to different health conditions of the centrifugal pump are acquired. Vibration signals obtained from the centrifugal pump carry a great deal of information and generally, statistical features are extracted from the vibration signals to retain meaningful fault information. However, these features are either insensitive to weak incipient faults or unsuitable for tracking severe faults, thus, decreasing the fault classification accuracy. To tackle this problem, a signal to time-frequency imaging technique is applied to the pump vibration signals. For this purpose, Continuous Wavelet Transform (CWT) is applied to decompose the vibration signals over different time-frequency scales and extract the pump fault information in both the time and frequency domains. The CWT scales form two-dimensional time-frequency images commonly referred to as scalograms. The CWT scalograms are then converted into grayscale images (SGI). Over the past few decades, CNN models have been established as an effective practice to process images for classification and pattern recognition. Consequently, the extracted CWTSGIs are finally provided as inputs to the proposed ADCNN architecture to achieve feature extraction and classification for centrifugal pump faults. The performance of the proposed diagnostic framework (CWTSGI + ADCNN) is validated with a vibration dataset collected from a testbed specifically designed for centrifugal pump diagnosis. The experimental results suggest that the proposed technique based on CWTSGI and ADCNN outperformed existing methods with an average performance improvement of 4.7 - 15.6%

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for continuous operations in machines which can also improve productivity and enhance system safety. In condition-based maintenance (CBM), effective diagnostics and prognostics are important aspects of CBM which provide sufficient time for maintenance engineers to schedule a repair and acquire replacement components before the components finally fail. All machine components have certain characteristics of failure patterns and are subjected to degradation processes in real environments. This paper describes a technique for accurate assessment of the remnant life of machines based on prior expert knowledge embedded in closed loop prognostics systems. The technique uses Support Vector Machines (SVM) for classification of faults and evaluation of health for six stages of bearing degradation. To validate the feasibility of the proposed model, several fault historical data from High Pressure Liquefied Natural Gas (LNG) pumps were analysed to obtain their failure patterns. The results obtained were very encouraging and the prediction closely matched the real life particularly at the end of term of the bearings

A centrifugal pump fault diagnosis approach based on LCD-ApEn and PNN

According to the non-stationary and nonlinear characteristics of bearing vibration signals of the centrifugal pumps, a fault diagnosis method based on local characteristic-scale decomposition (LCD)-approximate entropy (ApEn) and probabilistic Neural Network (PNN) is proposed in this paper. First, the vibration signals are decomposed into a finite number of intrinsic scale components (ICSs), after which the ApEn of each ISC is obtained to form the feature vector. Then, the feature vectors are selected as the input vectors of the PNN for fault diagnosis. The classification results achieve a fault recognition rate of 100 % for various centrifugal pump fault patterns, which verifies the effectiveness and feasibility of the proposed method

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables