On the classification of motive power failures

The Aim of the article is to develop a motive power failure classification to enable substantiated definition of dependability requirements for motive power as a part of a railway transportation system, as well as for organizing systematic measures to ensure a required level of its dependability over the life cycle. Methods. The terminology of interstate dependability-related standards was analysed and the two classifications used by OJSC “RZD” for estimating the dependability of technical systems and motive power were compared. The dependability of railway transportation systems is studied using structural and logical and logical and probabilistic methods of dependability analysis, while railway lines are examined using the graph theory and the Markov chains. Results. An analysis of the existing failure classifications identified shortcomings that prevent the use of such classifications for studying the structural dependability of such railway transportation systems as motive power. A classification was developed that combines two failure classifications (“category-based” for the transportation process and technical systems and “type-based” for the motive power), but this time with new definitions. The proposed classification of the types of failures involves stricter definitions of the conditions and assumptions required for evaluating the dependability and technical condition of an item, which ensures correlation between the characteristics of motive power and its dependability throughout the life cycle in the context of the above tasks. The two classifications could be used simultaneously while researching structural problems of dependability using logical and probabilistic methods and Markov chains. The developed classification is included in the provisions of the draft interstate standard “Dependability of motive power. Procedure for the definition, calculation methods and supervision of dependability indicators throughout the life cycle” that is being prepared by JSC “VNIKTI” in accordance with the OJSC “RZD” research and development plan. Conclusion. The article’s findings will be useful to experts involved in the evaluation of motive power dependability.

The Analysis Performance of Heart Failure Classification by Using Machine Learning Techniques

Heart failure means that the heart is not pumping well as normal as it should be. A congestive heart failure is a form of heart failure that involves seeking timely medical care, although the two terms are sometimes used interchangeably. Heart failure happens when the heart muscle does not pump blood as well as it can, often referred to as congestive heart failure. Some disorders, such as heart's narrowed arteries (coronary artery disease) or high blood pressure, eventually make the heart too weak or rigid to fill and pump effectively. Early detection of heart failure by using data mining techniques has gained popularity among researchers. This research uses some classification techniques for heart failure classification from medical data. This research analyzed the performance of some classification algorithms, namely Support Vector Machine (SVM), Decision Forest (DF), and Boosted Decision Tree (BDT), to classify accurately heart failure risk data as input. The best algorithm among the three is discovered for heart failure classification at the end of this research.

A Concept of Risk Prioritization in FMEA Analysis for Fluid Power Systems

FMEA analysis is a tool of quality improvement that has been widely used for decades. Its classical version prioritizes risk of failure by risk priority number (RPN). The RPN is a product of severity (S), occurrence (O), and detection (D), where all of the factors have equal levels of significance. This assumption is one of the most commonly criticized drawbacks, as it has given unreasonable results for real-world applications. The RPN can produce equal values for combinations of risk factors with different risk implications. Another issue is that of the uncertainties and subjectivities of information employed in FMEA analysis that may arise from lack of knowledge, experience, and employed linguistic terms. Many alternatives of risk assessment methods have been proposed to overcome the weaknesses of classical FMEA risk management in which we can distinguish methods of modification of RPN numbers of employing new tools. In this study, we propose a modification of the traditional RPN number. The main difference is that severity and occurrence are valued based on subfactors. The detection number remained unchanged. Additionally, the proposed method prioritizes risk in terms of implied risk to the systems by implementing functional failures (effects of potential failures). A typical fluid power system was used to illustrate the application of this method. The method showed the correct failure classification, which meets the industrial experience and other research results of failures of fluid power systems.

Different outcomes after proximal femoral replacement in oncologic and failed revision arthroplasty patients - a retrospective cohort study

Background Proximal femoral replacement (PFR) is a technically demanding procedure commonly performed to restore extensive, oncological or non-oncological bone defects in a severely debilitated patient collective. Depending on different indications, a varying outcome has been reported. The aim of the study was to assess the functional outcomes and complication rates of PFR with the modular Munich-Luebeck (MML) femoral megaprosthesis (ESKA/Orthodynamics, Luebeck, Germany), and to highlight outcome differences in patients treated for failed revision total hip arthroplasty (THA) or malignant bone disease. Methods A retrospective review of patients treated with PFR for failed THA or malignant tumor disease between 2000 and 2012 was performed. Patient satisfaction, functional outcome (VAS, SF-12, MSTS, WOMAC, TESS), complications and failure types (Henderson’s failure classification) were assessed. A Kaplan-Meier analysis determined implant survival. Results Fifty-eight patients (age: 69.9 years, BMI: 26.7 kg/m2, mean follow-up: 66 months) were included. The mean SF-12 (physical / mental) was 37.9 / 48.4. MSTS averaged 68% at final follow-up, while mean WOMAC and TESS scored 37.8 and 59.5. TESS and WOMAC scores demonstrated significantly worse outcomes in the revision group (RG) compared to the tumor group (TG). Overall complication rate was 43.1%, and dislocation was the most common complication (27.6%). Implant survival rates were 83% (RG) and 85% (TG; p = n.s.) at 5 years, while 10-year survival was 57% (RG) and 85% (TG, p < 0.05). Conclusions PFR is a salvage procedure for restoration of mechanical integrity and limb preservation after extensive bone loss. Complications rates are considerably high. Functional outcomes and 10-year implant survival rate were worse in the RG compared to the TG. Strict indications and disease-specific patient education are essential in preoperative planning and prognosis.

Convolutional Neural Network for Wind Turbine Failure Classification Based on SCADA Data

As a renewable energy source and an alternative to fossil fuels, the wind power industry is growing rapidly. However, due to harsh weather conditions, wind turbines (WT) still face many failures that raise the price of energy produced and reduce the reliability of wind energy. Hence, the use of reliable monitoring and diagnostic systems of WTs is of great importance. Operation and maintenance expenses represent 30% of the total cost of large wind farms. The installation of offshore and remote wind farms has increased the need for efficient fault detection and condition monitoring systems. In this work, without using specific custom devices for monitoring conditions, but only increasing the sampling frequency in the sensors already available (in all commercial WT) of the supervisory control and data acquisition system (SCADA), datadriven multiple fault detection is performed, and a classification strategy is developed. The data is processed, and subsequently, using a convolutional neural network (CNN), six faults are classified and evaluated with different metrics. Finally, it should be noted that the classification speed allows the implementation of this strategy to monitor conditions online in real under-production WTs.

Simultaneous Burr and Cut Interruption Detection during Laser Cutting with Neural Networks

In this contribution, we compare basic neural networks with convolutional neural networks for cut failure classification during fiber laser cutting. The experiments are performed by cutting thin electrical sheets with a 500 W single-mode fiber laser while taking coaxial camera images for the classification. The quality is grouped in the categories good cut, cuts with burr formation and cut interruptions. Indeed, our results reveal that both cut failures can be detected with one system. Independent of the neural network design and size, a minimum classification accuracy of 92.8% is achieved, which could be increased with more complex networks to 95.8%. Thus, convolutional neural networks reveal a slight performance advantage over basic neural networks, which yet is accompanied by a higher calculation time, which nevertheless is still below 2 ms. In a separated examination, cut interruptions can be detected with much higher accuracy as compared to burr formation. Overall, the results reveal the possibility to detect burr formations and cut interruptions during laser cutting simultaneously with high accuracy, as being desirable for industrial applications.