No abstract

Bayesian estimation techniques are finding application domains in machinery fault diagnosis and prognosis of the remaining useful life of a failing component/subsystem. This paper introduces a methodology for accurate and precise prediction of a failing component based on particle filtering and learning strategies. This novel approach employs a state dynamic model and a measurement model to predict the posterior probability density function of the state, i.e., to predict the time evolution of a fault or fatigue damage. It avoids the linearity and Gaussian noise assumption of Kalman filtering and provides a robust framework for long-term prognosis while accounting effectively for uncertainties. Correction terms are estimated in a learning paradigm to improve the accuracy and precision of the algorithm for long-term prediction. The proposed approach is applied to a crack fault and the results support its robustness and superiority.

We have isolated various missense mutations in the essential grpE gene of Escherichia coli based on the inability to propagate bacteriophage lambda. To better understand the biochemical mechanisms of GrpE action in various biological processes, six mutant proteins were overexpressed and purified. All of them, GrpE103, GrpE66, GrpE2/280, GrpE17, GrpE13a and GrpE25, have single amino acid substitutions located in highly conserved regions throughout the GrpE sequence. The biochemical defects of each mutant GrpE protein were identified by examining their abilities to: (i) support in vitro lambda DNA replication; (ii) stimulate the weak ATPase activity of DnaK; (iii) dimerize and oligomerize, as judged by glutaraldehyde crosslinking and HPLC size chromatography; (iv) interact with wild‐type DnaK protein using either an ELISA assay, glutaraldehyde crosslinking or HPLC size chromatography. Our results suggest that GrpE can exist in a dimeric or oligomeric form, depending on its relative concentration, and that it dimerizes/oligomerizes through its N‐terminal region, most likely through a computer predicted coiled‐coil region. Analysis of several mutant GrpE proteins indicates that an oligomer of GrpE is the most active form that interacts stably with DnaK and that the interaction is vital for GrpE biological function. Our results also demonstrate that both the N‐terminal and C‐terminal regions are important for GrpE function in lambda DNA replication and its co‐chaperone activity with DnaK.

This study aims at exploring ice cream meltdown behavior by changing the levels of stabilizer (ST), polysorbate 80 (PS80), and overrun (OR). By adjusting the formulation of ice cream, the degree of fat destabilization (FD), mix viscosity (MV), and overrun can be controlled within a certain range, which in turn presents different meltdown behaviors for study. In addition to the drip‐through test, the shape of ice cream as it melts was recorded as height change to further investigate ice cream meltdown. Mix viscosity (at 50 s−1) and fat destabilization were found to have a significant effect not only on drip‐through rate, but also the induction time, final weight of the drip‐through part, height‐change rate, and final height of melted ice cream. On the other side, overrun was found only to have an effect on meltdown when no stabilizers were added. These results indicate serum phase viscosity (mix viscosity) and fat destabilization are important parameters to describe ice cream meltdown. Besides, the entire ice cream meltdown curve and height collapse curve provide important information on ice cream meltdown behavior. Practical application A new direction of analysis of ice cream meltdown behavior is provided in this study. The induction time, the final drip‐through weight, and the height change during the meltdown process were found to be the indicators on the influence of microstructure on ice cream meltdown behavior for the future study.

Planetary gear trains are complex flight critical components of helicopters and other aircraft. Failure modes on such components may lead to loss of life and/or aircraft. It is essential, therefore, that incipient failures or faults be detected and isolated as early as possible and corrective action be taken in order to avoid catastrophic events. Research thus far has focused on gear teeth faults and available methods could not detect a crack in the planetary gear plate under all operating conditions. A wavelet domain methodology is suggested for the analysis and feature extraction of the vibration data from the planetary gear system of military helicopters. Complex Morlet wavelets are employed and the time domain knowledge, preserved by the wavelet decomposition, is used to extract useful features that distinguish between faulted and healthy gear plates from experimental data made available from both on-aircraft and test cell experiments. A statistical method based on the z-test is also suggested to evaluate the relative performance of these features.