Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

“…In the time–frequency domain, Wavelet transform (WT) can be used to extract candidate feature parameters. The Wavelet packet transform (WPT) conducts a multilevel band division over the entire signal band, which not only inherits the advantages of the good time–frequency localization from the WT, but it also further decomposes the high-frequency band to increase the frequency resolution [ 4 , 22 , 34 ]. Thus, the WPT was applied in order to extract the time–frequency domain features in this paper, and the wavelet energy feature is the energy of a 3-level wavelet packet decomposition using db1, which corresponds to the wavelet coefficient with a higher energy that is related to the characteristic frequency of the machine [ 46 ].…”

“…Multiple sensor-based methods can enhance the richness of information that contains potential tool wear levels [ 18 ]. Although multisensor setups provide more redundant information, they can reduce the overall uncertainty of the measurement and improve the resolution and accuracy of the TCM system [ 4 , 19 , 20 ].…”

“…Sohyung et al [ 36 ] constructed 135 feature parameters in the time and frequency domains of multiple sensory signals and selected 25 feature parameters by employing the entropy correlation algorithm. Liu et al [ 4 ] extracted 138 feature parameters (including time domain, frequency domain, and wavelet energy) as a candidate feature parameter set and used the fast correlation filter (FCBF) algorithm to establish the smallest redundant feature set, including 19 feature parameters. Feature selection methods can reduce the number of input parameters of the monitoring model by screening out the characteristic parameters that have a strong correlation with the tool state, but they also reduce the computation of the online monitoring stage.…”

“…Tool breakage has negative direct (capital) and indirect (time loss) effects. The downtime of a milling machine due to tool failure accounts for 7–20% of total downtime [ 2 , 3 ], and the cost of tools and tool changes account for 3–12% of the total processing cost [ 4 ]. As the conditions of tools vary over time, the timeliness of detecting tool damage is critical, and it requires an appropriate tool replacement strategy.…”

A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis

“…However, despite the fact that the overall Silhouette value (i.e., 0.8258) and also the C-index value (i.e., 0.0194) and the DB value (i.e., 0.3262) indicate the goodness of the obtained clusters, this approach: 1) entails discarding the existing P * FF1 = 10 consensus clusters (representative of the FF1 plant turbine behaviors under different environmental and operational conditions) and computationcostly retraining the diagnostic tool with all data that have been accumulated thus far (i.e., N aggregatedFF1,EE1 = 149 + 116 = 265). This approach would result in a catastrophic forgetting of the acquired information contained in the P * FF1 = 10 consensus clusters [50,51], and hence, the detailed analysis of each plant will be discarded once the whole transients are aggregated together. For this reason, this approach cannot be used to predict the health state of new incoming NPP turbines, 2) is considered infeasible for real diagnostic systems due to the computational efforts required for retraining on a large number of transients from a large number of plants [9].…”