A convolutional neural network aided physical model improvement for AC solenoid valves diagnosis

This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves. However, the approaches do not give a physical explanation of the failure modes. In this work, being capable of diagnosing failure modes while using a physically interpretable model is proposed. Feature attribution methods are applied to CNN on a large data set of the current signals acquired from accelerated life tests of several AC solenoid valves. The results reveal important regions of interest on current signals that guide the modeling of the main missing component of an existing physical model. Two model parameters, which are the shading ring and kinetic coulomb forces, are then identified using current measurements along the lifetime of valves. Consistent trends are found for both parameters allowing to diagnose the failure modes of the solenoid valves. Future work will consist of not only diagnosing the failure modes, but also of predicting the remaining useful life

Conventionalisation and discrimination as competing pressures on continuous speech-like signals

© John Benjamins Publishing Company. Arbitrary communication systems can emerge from iconic beginnings through processes of conventionalisation via interaction. Here, we explore whether this process of conventionalisation occurs with continuous, auditory signals. We conducted an artificial signalling experiment. Participants either created signals for themselves, or for a partner in a communication game. We found no evidence that the speech-like signals in our experiment became less iconic or simpler through interaction. We hypothesise that the reason for our results is that when it is difficult to be iconic initially because of the constraints of the modality, then iconicity needs to emerge to enable grounding before conventionalisation can occur. Further, pressures for discrimination, caused by the expanding meaning space in our study, may cause more complexity to emerge, again as a result of the restrictive signalling modality. Our findings have possible implications for the processes of conventionalisation possible in signed and spoken languages, as the spoken modality is more restrictive than the manual modality

Using leap motion to investigate the emergence of structure in speech and language

© 2016, The Author(s). In evolutionary linguistics, experiments using artificial signal spaces are being used to investigate the emergenceof speech structure. These signal spaces need to be continuous, non-discretized spaces from which discrete unitsand patterns can emerge. They need to be dissimilar from—but comparable with—the vocal tract, in order tominimize interference from pre-existing linguistic knowledge, while informing us about language. This is a hardbalance to strike. This article outlines a new approach that uses the Leap Motion, an infrared controller that canconvert manual movement in 3d space into sound. The signal space using this approach is more flexible than signalspaces in previous attempts. Further, output data using this approach is simpler to arrange and analyze. Theexperimental interface was built using free, and mostly open- source libraries in Python. We provide our sourcecode for other researchers as open source

Data-driven virtual sensing for probabilistic condition monitoring of solenoid valves

There is an emerging industrial demand for predictive maintenance algorithms that exhibit high levels of predictive accuracy. Such condition monitoring tools must estimate dynamic quantities, such as Remaining Useful Lifetime (RUL) and the State of Health (SOH), based on a, typically, restricted set of measurements that can be obtained in an operational setting. These quantities exhibit inherent stochasticity and can only be approximately determined a posteriori to system failure. This paper proposes a generic prognostic tool for probabilistic condition monitoring of mechatronic systems, with the aim to improve the probabilistic prediction of condition metrics, specifically RUL and SOH. Therefore we propose to identify a Hidden Markov Model (HMM) from a fully instrumented measurement set, that is only available for a restricted set of run-to-failure experiments, typically gathered in an R & D setting. Although being artificial and retrospectively constructed metrics, we interpret RUL and SOH as physical measurements with the purpose to identify accurate degradation dynamics. Once the degradation model is identified, we practice the mathematical flexibility of the HMM framework to estimate several of the no longer available dynamic quantities of interest in real-time, from the limited set of measurements that are available in an operational setting. This modelling paradigm is known as virtual sensing. Predictive performance and computational efficiency are further improved by domain knowledge based pre-processing of the measurements. We apply our methodology to solenoid valves (SV), a widely used and often critical component in many industrial systems, which display a large variation in useful lifetime. Benchmark results show that the predictive capabilities of the presented methodology compares with prognostic techniques that are more computationally and memory demanding. Note to Practitioners-The motivation for this research is twofold. First there is a pending industrial need for improved diagnostic and prognostic tools. Second there is the observation that lifetime tests usually take place in an R & D setting and that expert labelling of Remaining Useful Lifetime (RUL) or State of Health (SOH) of a component or system is often based on measurement data that is not available in the industrial setting where the prognostic tools are to be deployed in the end. These two observations suggest that there is large potential in methods that can correlate the expert labelling, in particular RUL & SOH signals, with measurement data that is available in the industrial setting. Our approach has been tested in detail on the case of Solenoid Valves, which are widely used in industry and that are often safety critical. Our experiments demonstrate that the method compares with brute force approaches that overpower ours both in terms of computational as well as memory requirements. The method is furthermore generic and there is no reason to assume it would not work for other applications

Comparison of Fat Oxidations Determined in Fat Max Intensity in Athletes and Sedentary Individuals

WOS: 000453220100044

Comparision of fat oxidation rate at crossover point during exercise among women with different body mass index

WOS: 000458742100012Purpose: The aim of this study was to evaluate the relationship between metabolic responses and fat oxidation at basal metabolic rate (BMR) and crossover point during performance tests in sedentary young women with different body mass index (BMI). Materials and Methods: Thirty sedentary women who were classified as normal weight (n=10), overweight (n=10) and class I obese (n=10) according to their body mass index participated in this study. Participants' basal metabolic rate and metabolic responses during exercise were measured by indirect calorimetry (Quark B2). Exercises were performed with gradually increased test protocols on treadmill. Results: Body mass index of groups were significantly different than each other. Body weight normalized peak oxygen uptake value reduced significantly with weight gain. Fat oxidation rate at crossover point increased with weight gain and the difference between normal and class-I obese women was found to be significant. Comparison of energy expenditure at crossover point for normal weight group was found to be significantly lower than overweight and class-I obese women. Conclusion: Fat oxidation rates and total energy expenditure increases with body mass index at crossover point

Oxidative DNA Damage to Sperm Cells and Peripheral Blood Leukocytes in Infertile Men

Background: Oxidative DNA damage is associated with male infertility. The aim of this study was to evaluate the oxidative DNA damage of sperm cells and blood leukocytes and to determine the levels of MDA and NO levels in seminal and blood plasma of idiopathic infertile men