This paper addresses self-diagnostic properties of active magnetic bearing (AMB) supported rotors for online detection of the transverse crack on a rotating shaft. In addition to pure levitation, the rotor supporting bearing also serves as an actuator that transforms current signals additionally injected into the control loop into the superimposed specially selected excitation forces into the suspended rotor. These additional excitations induce combination frequencies in the rotor response, providing unique signatures for the presence of crack. The background of theoretical modeling, experimental, and computer simulation results for the AMB supported cracked rotor with self-diagnostic excitation forces are presented and discussed.

In previous studies, empirical models have been widely used to map water quality parameters for inland waters using remotely sensed imagery. Due to the complex optical properties of inland waters, empirical models often have limited performances and they cannot be extended or reused across space and time. To overcome the limitations of traditional empirical models, this article further explores the ensemble method for water quality parameter retrieval. Based on the Sentinel-2 multispectral images and extensive coincident in situ water quality data, we examined different implementation strategies and evaluated the extensibility of a multipredictor ensemble model for retrieving chlorophyll-a (Chl-a) across space and time. Our analysis shows that the multipredictor ensemble model can improve the Chl-a prediction accuracy by 46% compared with the best traditional empirical model. Our experiments suggest that the selection of heterogeneous component models improves the performance of the ensemble model and the new iterative $K$ -fold calibration approach results in a considerably better ensemble model than the global calibration approach. To evaluate the spatial extensibility, our ensemble model calibrated for Harsha Lake was applied to Caesar Creek Lake and Brookville Lake. This ensemble model calibrated with the data sets acquired on October 7, 2016 was also applied to Sentinel-2 images of Harsha Lake acquired on October 2, 2017 and August 23, 2018, to evaluate its extensibility for different years. Our evaluation results show that the ensemble model can be transferred and reused across space and time to provide reliable and accurate Chl-a estimates.

Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies.

This paper addresses self-diagnostic properties of AMB (active magnetic bearing) supported rotors for on-line detection of the transverse crack on a rotating shaft. In addition to pure levitation, the rotor supporting bearing also serves as an actuator that transforms current signals additionally injected into the control loop into the superimposed specially selected excitation forces into the suspended rotor. These additional excitations induce combination frequencies in the rotor response, providing unique signatures for the presence of crack. The background of theoretical modeling, experimental and computer simulation results for the AMB supported cracked rotor with self-diagnostic excitation forces are presented and discussed.