scholarly journals Data Driven Soft Sensor for Condition Monitoring of Sample Handling System (SHS)

2020 ◽  
Author(s):  
Abhilash Pani ◽  
Jinendra Gugaliya ◽  
Mekapati Srinivas
Keyword(s):  
Condition Monitoring ◽  
Soft Sensor ◽  
Data Driven ◽  
Support Vector ◽  
Sensor Failure ◽  
Sample Handling ◽  
Handling System ◽  
Impact Performance ◽  
True Value ◽  
Condition Monitoring Systems

Gas sample is conditioned using sample handling system (SHS) to remove particulate matter and moisture content before sending it through Continuous Emission Monitoring (CEM) devices. The performance of SHS plays a crucial role in reliable operation of CEMs and therefore, sensor-based condition monitoring systems (CMSs) have been developed for SHSs. As sensor failures impact performance of CMSs, a data driven soft-sensor approach is proposed to improve robustness of CMSs in presence of single sensor failure. The proposed approach uses data of available sensors to estimate true value of a faulty sensor which can be further utilized by CMSs. The proposed approach compares multiple methods and uses support vector regression for development of soft sensors. The paper also considers practical challenges in building those models. Further, the proposed approach is tested on industrial data and the results show that the soft sensor values are in close match with the actual ones.

A novel approach for data-driven process and condition monitoring systems on the example of mill-turn centers

2018 ◽  
Vol 12 (3-4) ◽  
pp. 525-533 ◽  
Author(s):  
Dominik Kißkalt ◽  
Hans Fleischmann ◽  
Sven Kreitlein ◽  
Manuel Knott ◽  
Jörg Franke
Keyword(s):  
Condition Monitoring ◽  
Data Driven ◽  
Monitoring Systems ◽  
Novel Approach ◽  
Condition Monitoring Systems

Data-driven soft sensor modeling based on twin support vector regression for cane sugar crystallization

2019 ◽  
Vol 241 ◽  
pp. 159-165 ◽  
Author(s):  
Yanmei Meng ◽  
Qiliang Lan ◽  
Johnny Qin ◽  
Shuangshuang Yu ◽  
Haifeng Pang ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Cane Sugar ◽  
Soft Sensor ◽  
Data Driven ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Sensor Modeling

SENSOR-FAULT TOLERANT CONDITION MONITORING OF AN INDUSTRIAL MACHINE

2013 ◽  
Vol 09 (01) ◽  
pp. 1350001 ◽  
Author(s):  
SRINIVAS RAMAN ◽  
CLARENCE W. DE SILVA
Keyword(s):  
Condition Monitoring ◽  
Fault Tolerant ◽  
Wavelet Packet ◽  
Experimental System ◽  
Feature Reduction ◽  
Support Vector ◽  
Sensor Failure ◽  
Failure Characteristics ◽  
Machine Vibration ◽  
Vector Machines

In this paper, a multi-sensor condition monitoring scheme is developed to diagnose machine faults in the presence of sensor failure. The signals from the monitored machine are decomposed using the wavelet packet transform (WPT). Two feature reduction schemes, using genetic algorithms are developed for feature selection in condition monitoring. One scheme assumes no prior knowledge about system costs or failure characteristics, and the other scheme aims to minimize the operating costs over a period of time. Two classifiers, radial basis function networks and support vector machines, are developed and compared in their ability to classify machine faults under conditions of sensor failure. The developed methodology is implemented in an experimental system, an industrial fish processing machine. The machine is instrumented with multiple accelerometers and microphones to continuously acquire signals of machine vibration and sound. The performance of the implemented fault diagnosis methodology is evaluated though experimentation.

scholarly journals Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Teng Wang ◽  
Zheng Liu ◽  
Guoliang Lu
Keyword(s):  
Condition Monitoring ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Data Driven ◽  
Time Frequency ◽  
Vibration Data ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring Systems ◽  
Early Fault Detection ◽  
Condition Indicator

Most condition monitoring systems rely on system-driven generation of indicators or features for early fault detection. However, this strategy requires the prior knowledge on the system kinematics and/or exact structure parameters of monitored system. To address this problem, this paper presents a novel condition monitoring framework where the condition indicator is generated via data-driven method. In this framework, the time-frequency periodogram is extracted from raw vibration signal first. Then, the acquired time-frequency periodogram is mapped by pseudo Perron vector, which is learned from vibration data, to generate the condition indicator. Finally, the bearing can be monitored via analyzing this indicator using gaussian based control chart. Based on experimental results on a publicly-available database, we show the effectiveness of presented framework for early fault detectionin the continuous operation of rolling bearing, indicating its great potentials in real engineering applications.

scholarly journals A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2142 ◽  
Author(s):  
Cong Yang ◽  
Zheng Qian ◽  
Yan Pei ◽  
Lu Wei
Keyword(s):  
Condition Monitoring ◽  
Low Cost ◽  
Rapid Development ◽  
Data Driven ◽  
Support Vector ◽  
Data Driven Approach ◽  
Pitch System ◽  
Adaptive Ability ◽  
Selection Algorithms ◽  
Residual Errors

With the rapid development of wind energy, it is important to reduce operation and maintenance (O&M) costs of wind turbines (WTs), especially for a pitch system, which suffers the highest failure rate and downtime. This paper proposes a data-driven method for pitch- system condition monitoring (CM) by only using supervisory control and data acquisition (SCADA) data without any faults, which could be applied to reduce O&M costs of pitch system by providing fault alarms. The pitch-motor temperature is selected as the indicator, and three feature-selection algorithms are employed to select the most appropriate input parameters for modeling. Six data-driven algorithms are applied to model pitch-motor temperature and the support vector regression (SVR) model has the highest accuracy. The control-chart method based on the residual errors between model output and measured value is utilized to calculate the outliers, thus the abnormal condition could be clearly identified once the outliers appear for a period of time. The effectiveness of the proposed method is demonstrated by several case studies, and compared with the classification models. Due to the adaptive ability and low cost, the proposed approach is suitable for online CM of pitch systems, and provides a strategy for CM of new WTs.

Data Driven Fault Detection of Premixer Centerbody Degradation in a Swirl Combustor

2020 ◽  
Author(s):  
Raghul Manosh Kumar ◽  
Benjamin Peters ◽  
Benjamin Emerson ◽  
Kamran Paynabar ◽  
Nagi Gebraeel ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Gas Turbines ◽  
Data Curation ◽  
Classification Model ◽  
Data Driven ◽  
Monitoring Systems ◽  
Feature Engineering ◽  
Data Set ◽  
Swirl Combustor ◽  
Condition Monitoring Systems

Abstract This paper introduces a data-driven framework for combustor-focused, performance-based condition monitoring of gas turbines. Commercial condition monitoring systems typically generate huge amounts of data that make efficient onboard monitoring challenging. This paper focuses on quantifying combustor component degradation, using premixer centerbody degradation in a swirl stabilized combustor as a case study. The input for these analyses is acoustic pressure measurements acquired at various locations on the combustor. The diagnosis methodology is based on a classification framework and consists of 3 steps: 1) Data curation, 2) Feature Engineering, and 3) Diagnosis. Data curation ensures good quality of the data that is passed through the algorithm. Feature engineering deals with the extraction of the most informative features, from the most informative sensors, that can accurately capture the introduced fault. To perform diagnosis, the classification model is trained using experimentally acquired data and is then tested on a separate data set. The framework was able to achieve high classification accuracy (>99%) for training size as low as 30% of the total recorded observations. The low number of features required to achieve this accuracy suggests high potential for integration into existing onboard condition monitoring systems.

scholarly journals SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies

2020 ◽  
Vol 10 (23) ◽  
pp. 8685
Author(s):  
Ravi Pandit ◽  
Athanasios Kolios
Keyword(s):  
Support Vector Machine ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Offshore Wind Turbine ◽  
Power Curve ◽  
Power Curves

Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.

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