A Generic Software Architecture For Prognostics

Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. As a result, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use

Blood test shows high accuracy in detecting stage I non-small cell lung cancer.

BACKGROUND: In a previous study (Goebel et. al, Cancer Genomics Proteomics 16:229-244, 2019), we identified 33 biomarkers for an early stage (I-II) Non-Small Cell Lung Cancer (NSCLC) test with 90% accuracy, 80.3% sensitivity, and 95.4% specificity. For the current study, we used a narrowed ensemble of 21 biomarkers while retaining similar accuracy in detecting early stage lung cancer. METHODS: A multiplex platform, 486 human plasma samples, and 21 biomarkers were used to develop and validate our algorithm which detects early stage NSCLC. The training set consisted of 258 human plasma with 79 Stage I-II NSCLC samples. The 21 biomarkers with the statistical model (Lung Cancer Detector Test 1, LCDT1) was then validated using 228 novel samples which included 55 Stage I NSCLC. RESULTS: The LCDT1 exhibited 95.6% accuracy, 89.1% sensitivity, and 97.7% specificity in detecting Stage I NSCLC on the blind set. When only NSCLC cancers were analyzed, the specificity increased to 99.1%. CONCLUSIONS: Compared to current approved clinical methods for diagnosing NSCLC, the LCDT1 greatly improves accuracy while being non-invasive; a simple, cost-effective, early diagnostic blood test should result in expanding access and increase survival rate

Diagnosis of Non-small Cell Lung Cancer for Early Stage Asymptomatic Patients.

BACKGROUND/AIM: In 2016 in the United States, 7 of 10 patients were estimated to die following lung cancer diagnosis. This is due to a lack of a reliable screening method that detects early-stage lung cancer. Our aim is to accurately detect early stage lung cancer using algorithms and protein biomarkers. PATIENTS AND METHODS: A total of 1,479 human plasma samples were processed using a multiplex immunoassay platform. 82 biomarkers and 6 algorithms were explored. There were 351 NSCLC samples (90.3% Stage I, 2.3% Stage II, and 7.4% Stage III/IV). RESULTS: We identified 33 protein biomarkers and developed a classifier using Random Forest. Our test detected early-stage Non-Small Cell Lung Cancer (NSCLC) with a 90% accuracy, 80% sensitivity, and 95% specificity in the validation set using the 33 markers. CONCLUSION: A specific, non-invasive, early-detection test, in combination with low-dose computed tomography, could increase survival rates and reduce false positives from screenings

SILHIL Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines

Software-in-the-loop and Hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The creation of an offline framework for verifying and validating supervisory failure prognostics and decision making routines is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle

Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience-the ability to avoid or quickly recover from faults-is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures

Failure Analysis in Conceptual Phase toward a Robust Design: Case Study in Monopropellant Propulsion System

As a system becomes more complex, the uncertainty in the operating conditions increases. In such a system, implementing a precise failure analysis in early design stage is vital. However, there is a lack of applicable methodology that shows how to implement failure analysis in the early design phase to achieve a robust design. The main purpose of this paper is to present a framework to design a complex engineered system resistant against various factors that may cause failures, when design process is in the conceptual phase and information about detailed system and component is unavailable. Within this framework, we generate a population of feasible designs from a seed functional model, and simulate and classified failure scenarios. We also develop a design selection function to compare robust score for candidate designs, and produce a preference ranking. We implement the proposed method on the design of an aerospace monopropellant propulsion system

A protocol for ultra-high field laminar fMRI in the human brain.

Ultra-high field (UHF) neuroimaging affords the sub-millimeter resolution that allows researchers to interrogate brain computations at a finer scale than that afforded by standard fMRI techniques. Here, we present a step-by-step protocol for using UHF imaging (Siemens Terra 7T scanner) to measure activity in the human brain. We outline how to preprocess the data using a pipeline that combines tools from SPM, FreeSurfer, ITK-SNAP, and BrainVoyager and correct for vasculature-related confounders to improve the spatial accuracy of the fMRI signal. For complete details on the use and execution of this protocol, please refer to Jia et al. (2020) and Zamboni et al. (2020).This work was supported by grants to Z.K. from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), the Wellcome Trust (205067/Z/16/Z) and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 840271