ABSTRACT
The power system, as a core component of a launch vehicle, has a crucial impact on the reliability and safety of a rocket launch. Due to the limited measurement information inside the engine, it is often challenging to realize fast and accurate anomaly detection. For this reason, this paper introduces the rocket flight state data to expand the information source for anomaly detection. However, engine measurement and rocket flight state information have different data distribution characteristics. To find the optimal data fusion scheme for anomaly detection, a data set information fusion algorithm based on convex optimization is proposed, which solves the optimal fusion parameter using the convex quadratic programming problem and then adopts the adaptive CUSUM algorithm to realize the fast and accurate anomaly detection of engine faults. Numerical simulation tests show that the algorithm proposed in this paper has a higher detection accuracy and lower detection time than the traditional algorithm.
ABSTRACT
Degradation prognostics of aero-engine are a well-recognized challenging issue. Data-driven prognostic techniques have been receiving attention because they rely on neither expert knowledge nor mathematic model of the system. But they are highly dependent on the quantity and quality of degradation data. To solve the problems caused by unlabeled, unbalanced condition monitoring (CM) data and uncertainties of the prognostics process, a novel data-driven aero-engine degradation prognostic strategy is proposed in this article. First, two indicators are defined to remove redundant degradation features. Then, the number of discrete states of health is determined by a fuzzy c -means algorithm, and the health state labels can be automatically assigned for health state estimation, where the uncertain initial condition and the uncertainty of health state's transition are fully considered. Finally, a multivariate health estimation model and a multivariate multistep-ahead long-term degradation prediction model are proposed for remaining useful life estimation for aero-engines. Verification results using the aero-engine data from NASA can show that the proposed data-driven degradation prognostic strategy is effective and feasible.
ABSTRACT
Extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT) lymphoma is rare among lung neoplasia cases, representing only 0.5%-1% of newly diagnosed primary lung lymphoma. MALT lymphoma with relapsed refractory and malignant transformation is highly heterogeneous and consensus therapy remains undetermined. We report a 55 year-old woman with a 3 year history of primary pulmonary MALT lymphoma confined to the lung presenting with massive pleural effusion. After two cycles of R-CHOP and six cycles of R2-CHOP, pleural effusion disappeared but the pulmonary mass remained persistent. Second-line therapies R2-GemOx failed to make any substantial improvement. Core-needle puncture biopsy of the pulmonary mass was obtained and pathological testing revealed transformed diffuse large B-cell lymphoma of germinal center B-cell subtype. Next-generation sequencing confirmed BN2 subtype. The mass showed no reduction after three cycles of R-MINE, following which the BTK inhibitor ibrutinib was administered to this patient. Unfortunately, after two months of ibrutinib treatment, the patient rapidly developed an enlarged mass and hyperprogressive disease, to which she subsequently succumbed.
