Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors.

Failure mode detection is essential for bearing life prediction to protect the shafts on the machinery. This work demonstrates the rolling bearing vibration measurement, signals converting and analysis, feature extraction, and machine learning with neural networks to achieve failure mode detection for a shaft bearing. Two self-designed bearing test platforms with two types of sensors conduct the bearing vibration collection in normal and abnormal states. The time-domain signals convert to the frequency domain for analysis to observe the dominant frequency between these two types of sensors. In feature extraction, principal components analysis (PCA) combines with wavelet packet decomposition (WPD) to form the two feature extraction methods: PCA-WPD and WPD-PCA for optimization. The features extracted by these two methods serve as input to the long short-term memory (LSTM) networks for classification and training to distinguish bearing states in normal, misaligned, unbalanced, and impact loads. The evaluation arguments include sensor types, vibration directions, failure modes, feature extraction methods, and neural networks. In conclusion, the developed methods with the typical lower-cost sensor can achieve 97% accuracy in bearing failure mode detection.

A Practice of BLE RSSI Measurement for Indoor Positioning.

Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI-distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.

Sweet potato cysteine proteases SPAE and SPCP2 participate in sporamin degradation during storage root sprouting.

Sweet potato sporamins are trypsin inhibitors and exhibit strong resistance to digestion by pepsin, trypsin and chymotrypsin. In addition, they constitute the major storage proteins in the sweet potato and, after degradation, provide nitrogen as a nutrient for seedling regrowth in sprouting storage roots. In this report, four cysteine proteases-one asparaginyl endopeptidase (SPAE), two papain-like cysteine proteases (SPCP1 and SPCP2), and one granulin-containing cysteine protease (SPCP3)-were studied to determine their association with sporamin degradation in sprouting storage roots. Sporamin degradation became significant in the flesh of storage roots starting from week 4 after sprouting and this correlated with expression levels of SPAE and SPCP2, but not of SPCP1 and SPCP3. In the outer flesh near the skin, sporamin degradation was more evident and occurred earlier than in the inner flesh of storage roots. Degradation of sporamins in the outer flesh was inversely correlated with the distance of the storage root from the sprout. Exogenous application of SPAE and SPCP2, but not SPCP3, fusion proteins to crude extracts of the outer flesh (i.e., extracted from a depth of 0.3cm and within 2cm of one-week-old sprouts) promoted in vitro sporamin degradation in a dose-dependent manner. Pre-treatment of SPAE and SPCP2 fusion proteins at 95°C for 5min prior to their application to the crude extracts reduced sporamin degradation. These data show that sweet potato asparaginyl endopeptidase SPAE and papain-like cysteine protease SPCP2 participate in sporamin degradation during storage root sprouting.

Application of persulfate to remediate petroleum hydrocarbon-contaminated soil: feasibility and comparison with common oxidants.

In this study, batch experiments were conducted to evaluate the feasibility of petroleum-hydrocarbon contaminated soil remediation using persulfate oxidation. Various controlling factors including different persulfate and ferrous ion concentrations, different oxidants (persulfate, hydrogen peroxide, and permanganate), and different contaminants (diesel and fuel oil) were considered. Results show that persulfate oxidation is capable of treating diesel and fuel oil contaminated soil. Higher persulfate and ferrous ion concentrations resulted in higher diesel degrading rates within the applied persulfate/ferrous ion molar ratios. A two-stage diesel degradation was observed in the batch experiments. In addition, treatment of diesel-contaminated soil using in situ metal mineral activation under ambient temperature (e.g., 25°C) may be a feasible option for site remediation. Results also reveal that persulfate anions could persist in the system for more than five months. Thus, sequential injections of ferrous ion to generate sulfate free radicals might be a feasible way to enhance contaminant oxidation. Diesel oxidation efficiency and rates by the three oxidants followed the sequence of hydrogen peroxide>permanganate>persulfate in the limited timeframes. Results of this study indicate that the application of persulfate oxidation is a feasible method to treat soil contaminated by diesel and fuel oil.