Fuzzy Prognosis System for Decision Making to Vibrations Monitoring in Gas Turbine

This paper proposes a decision making approach based on the development of a fuzzy prognostic system to ensure the vibrations monitoring of a gas turbine based on real time information obtained from different installed sensors. In this approach the case of incomplete obtained data which may occur frequently is taken into account by using an approach of full data reconstitution form incomplete data. The proposed fuzzy prognostic system approach presented in this paper allows the analysis of the data obtained via the vibration indicators of a gas turbine system for the accurate identification of the faults to avoid the performance degradation of such systems. In order to prove the robustness of the proposed approach presented in this paper, several tested has been performed.

Science organization of management as a method of production systems’ effectiveness increasing

Modern high-tech production must be highly-effective and competitive, what is possible only when effective management system is available. There are disadvantages and problems of domestic machine-building enterprises and their practical experience. On the basis of made research with using fundamental theoretical and applied developments in the sphere of increasing management there is suggested conceptual appearance of a unique prognostic system of machine-building enterprises not mass production, the choice of directions of increasing these enterprises’ operative management system is justified. The purpose of the article is to propose ways to improve the efficiency of manufacturing enterprises. The methodology used in conducting this study and developing proposals for increasing the efficiency of production systems is based on the principles and methods of the general theory of management, its cybernetic and social components, as well as methods of specific management sciences for the creation and effective functioning of enterprise management mechanisms.

Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning

Background Integrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. Methods and findings Cancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10−23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman’s rank correlation coefficient = 0.8316; p-value = 4.5×10−13). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8×10−43). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems. Conclusions The EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.

A Case Study on the Detection and Prognosis of Internal Leakages in Electro-Hydraulic Flight Control Actuators

Electro-hydraulic servo-actuators (EHSAs) are currently considered the state-of-the art solution for the control of the primary flight control systems of civil and military aircraft. Combining the expected service life of a commercial aircraft with the fact that electro-hydraulic technology is employed in the vast majority of currently in-service aircraft and is planned to be used on future platforms as well, the development of an effective Prognostic and Health Management (PHM) system could provide significant advantages to fleet operators and aircraft maintenance, such as the reduction of unplanned flight disruptions and increased availability of the aircraft. The occurrence of excessive internal leakage within the EHSAs is one of the most common causes of return from the field of flight control actuators, making this failure mode a priority in the definition of any dedicated PHM routine. This paper presents a case study on the design of a prognostic system for this degradation mode, in the context of a wider effort toward the definition of a prognostic framework suitable to work on in-flight data. The study is performed by means of a high-fidelity simulation model supported by experimental activities. Results of both the simulation and the experimental work are used to select a suitable feature, then implemented within the prognostic framework based on particle filtering. The algorithm is at first theoretically discussed, and then tested against several degradation patterns. Performances are evaluated through state-of-the-art metrics, showing promising results and providing the basis towards future applications on real in-flight data.

Experimental Study of Vibration Signal for a Prognostic System to Prevent Tool Breakage in Micro Gun Drilling

Unexpected drill breakage can be foreseen and prevented. We observed a factory and identified the warning signs of tool breakage for micro gundrills, as well as a laboratory experiment for micro drills. The vibrations of stable drilling and the vibrations that warn of tool breakage were analyzed based on the time and frequency domain features. We developed a prognostic model. We conducted physical drilling experiments on a Swiss turning machine and a laboratory research platform. Stainless steel was drilled with two types of 0.9-mm-diameter tools: 125-mm-long micro gundrills on Swiss turning machine and 25-mm-long micro drills. In both types of testing, two accelerometers were installed on the tool holder to collect two-directional vibration signals; a linear discriminant function processed the Z-axis and Y-axis signals for the telltale warning signs of impending tool breakage, and obtained a 100% classification rate. To confirm the effect of drilling disturbances on the prognostic system, the entries and exits of tools to and from workpieces were studied. The results demonstrate that both types of signal features can be used without causing any misclassification.

MicroRNAs in the Myelodysplastic Syndrome

The myelodysplastic syndrome (MDS) holds a special place among blood cancers, as it represents a whole spectrum of hematological disorders with impaired differentiation of hematopoietic precursors, bone marrow dysplasia, genetic instability and is noted for an increased risk of acute myeloid leukemia. Both genetic and epigenetic factors, including microRNAs (miRNAs), are involved in MDS development. MicroRNAs are short non-coding RNAs that are important regulators of normal hematopoiesis, and abnormal changes in their expression levels can contribute to hematological tumor development. To assess the prognosis of the disease, an international assessment system taking into account a karyotype, the number of blast cells, and the degree of deficiency of different blood cell types is used. However, the overall survival and effectiveness of the therapy offered are not always consistent with predictions. The search for new biomarkers, followed by their integration into the existing prognostic system, will allow for personalized treatment to be performed with more precision. Additionally, this paper explains how miRNA expression levels correlate with the prognosis of overall survival and response to the therapy offered.

Proposal of a New Prognostic Model for Differentiated Thyroid Cancer with TERT Promoter Mutations

The role of telomerase reverse transcriptase (TERT) promoter mutations as an independent poor prognostic factor in differentiated thyroid cancer (DTC) patients is well known, but there is no prognostic system that combines the TERT promoter mutation status with tumor-node-metastasis (TNM) stage to predict cancer-specific survival (CSS). A total of 393 patients with pathologically confirmed DTC after thyroidectomy were enrolled. After incorporating wild-type TERT and mutant TERT with stages I, II, and III/IV of the AJCC TNM system 8th edition (TNM-8), we generated six combinations and calculated 10-year and 15-year CSS and adjusted hazard ratios (HRs) for cancer-related death using Cox regression. Then, a new mortality prediction model termed TNM-8T was derived based on the CSS and HR of each combination in the four groups. Of the 393 patients, there were 27 (6.9%) thyroid cancer-related deaths during a median follow-up of 14 years. Patients with a more advanced stage had a lower survival rate (10-year CSS for TNM-8T stage 1, 2, 3, and 4: 98.7%, 93.5%, 77.3%, and 63.0%, respectively; p < 0.001). TNM-8T showed a better spread of CSS (p < 0.001) than TNM-8 (p = 0.002) in the adjusted survival curves. The C-index for mortality risk predictability was 0.880 (95% CI, 0.665–0.957) in TNM-8T and 0.827 (95% CI, 0.622–0.930) in TNM-8 (p < 0.001). TNM-8T, a new prognostic system that incorporates the TERT mutational status into TNM-8, showed superior predictability to TNM-8 in the long-term survival of DTC patients.