Table 1 - uploaded by Kauko Leiviskä
Content may be subject to copyright.
![Cavitation index and vibration severity criteria [10].](profile/Kauko-Leiviskae/publication/307976774/figure/tbl1/AS:668378700197903@1536365308736/Cavitation-index-and-vibration-severity-criteria-10.png)
Source publication
![Table 1 . Cavitation index and vibration severity criteria [10].](profile/Kauko-Leiviskae/publication/307976774/figure/tbl1/AS:668378700197903@1536365308736/Cavitation-index-and-vibration-severity-criteria-10_Q320.jpg)
![Figure 2. The cavitation index for a Kaplan water turbine [10].](profile/Kauko-Leiviskae/publication/307976774/figure/fig1/AS:405118458580993@1473599175758/The-cavitation-index-for-a-Kaplan-water-turbine-10_Q320.jpg)
Early detection of fluctuations in operating conditions and fault detection can be done with similar methods. Signal processing is needed for the condition monitoring measurements, and interpolation is some process measurements and especially laboratory analysis. Effective time delays are very important in process data. Feature extraction uses stat...
Citations
... In the present systems, the scaling functions are developed by using generalised moments and norms (Juuso and Lahdelma, 2010;Juuso, 2013) and tuned with genetic algorithms (Juuso, 2009). The condition monitoring applications are similar with detecting operating conditions in the process industry (Juuso and Leiviskä, 2010). Process and condition monitoring data is combined in detecting operating conditions: measurements which require signal processing are denoted as signals, normal process measurements are directly used in feature extraction, and in addition some infrequent measurements need to be interpolated. ...
The early detection of fluctuations in operating conditions and fault detection is done with similar methods. The feature extraction uses statistical analysis based on generalised norms and moments. Intelligent stress indices are calculated from these features by nonlinear scaling. The scaling approach uses the norms and moments to produce indices, which are consistent with the vibration severity criteria. Nonlinear scaling can be used for finding suitable control limits for the features and indices. Harmful high levels of stress are efficiently detected with control limits adjusted to the process requirements. The limits can be explained by fuzzy set systems and categorical information is included through knowledge-based analysis. The statistical process control (SPC) can be extended to nonlinear and non-Gaussian data: the new generalised SPC is suitable for a large set of statistical distributions. It operates without interruptions in short run cases and adapts to the changing process requirements. The approach is tested in two application cases: a rolling mill and an underground load haul dump (LHD) machine.
... Intelligent methods extend the idea of dimensionless indices to nonlinear systems: the basic idea is nonlinear scaling, which was developed to extract the meanings of variables from measurement signals . In the present systems, the scaling functions are developed by using generalised moments and norms (Juuso and Lahdelma, 2010;Juuso, 2013) and tuned with genetic algorithms (Juuso, 2009 The condition monitoring applications are similar with detecting operating conditions in the process industry (Juuso and Leiviskä, 2010). Detection of operating conditions can be extended by means of a Case-Based Reasoning (CBR) type application with linguistic equation (LE) models and fuzzy logic (Juuso, 1994(Juuso, , 1999. ...
The papers published in these proceedings are presented in the International Conference on Maintenance, Condition Monitoring and Diagnostics, and Maintenance Performance Measurement and Management, MCMD 2015 and MPMM 2015, to be arranged in Oulu, Finland, in 30th September – 1st October, 2015.
Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present conference is supported by a variety of Finnish industrial enterprises.
... Degrees of membership for the activated cases can be used for estimating some process or product quality features. (25) ...
... Detecting operating conditions and faults(25) ...
Automatic fault detection with condition indices enables reliable condition monitoring to be combined with process control. Useful information on different faults can be obtained by selecting suitable features from generalised norms, which are defined by the order of derivation, the order of the norm and sample time. The nonlinear scaling based on generalised norms and skewness extends the idea of dimensionless indices to nonlinear systems and provides good results for the automatic generation of condition indices. Condition indices, which are used in the same way as the process measurements in process control, detect differences between normal and faulty conditions and provide an indication of the severity of the faults. Feature specific health indices, which are calculated as ratios of feature values in the reference condition and the faulty case, are used in selecting efficient features. In the multisensor vibration analysis, the number of sensors and features were drastically reduced. The number of features is further reduced by optimal orders for the derivatives and norms. The complexity of the models is simultaneously reduced. For the supporting rolls of a lime kiln, an efficient indication of faulty situations is achieved with two features. All the rolls can be analysed with the same approach throughout the data set. The results of both the applications are consistent with the vibration severity criteria: good, usable, still acceptable, and not acceptable. Three standard deviations obtained for the signal x(4) on three frequency ranges were needed to detect unbalance and bearing faults. The norms based on the signal x(4) provide the best results in all the frequency ranges.
Purpose
– The purpose of this paper is to develop a comprehensive approach to efficiently integrate maintenance and operation by combining process and condition monitoring data with performance measures.
Design/methodology/approach
– Intelligent stress, condition and health indicators have been developed for control and condition monitoring by combining generalised moments and norms with efficient nonlinear scaling. The data analysis resulting nonlinear scaling functions can also be used to handle performance measures used for management. The generalised norms provide limits for an advanced statistical process control.
Findings
– The data‐driven analysis methodology demonstrates that management‐oriented indicators can be presented in the same scale as intelligent condition and stress indices. Control, condition monitoring, maintenance and performance monitoring are represented as interactive feedback loops.
Practical implications
– Performance analysis can be based on real‐time information by using various stress, condition and health indices as inputs. Similar approaches can be used for outputs: quality indices, harmonised indices, key performance indicators, process capability indices and overall equipment effectiveness. Since consistent linguistic explanations based on nonlinear scaling are available for all these indices, the analysis can be further deepened with LE modelling. Efficient monitoring with intelligent indices provides a good basis for control and condition‐based maintenance and performance monitoring.
Originality/value
– The paper extends the nonlinear scaling methodology and linguistic equations to intelligent performance measures. The methodology provides a consistent way to also represent all information with linguistic terms.
