ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / The U.S. Air Force's next generation reusable booster (NGRSB) offers the opportunity for the Space Command to use intelligent equipment for decision making replacing personnel, increasing safety and mission assurance by removing decisions from program management personnel who may not have had any flight-test experience. Adding intelligence to launch vehicle and spacecraft equipment may include requiring the builder to use a prognostic and health management (PHM) program. The PHM was added to NASA's aircraft programs in 2009 and we have requested NASA HQ and NASA Marshal Space Flight Center adopt the NASA PHM in the procurement contracts used on the new Space Launch Systems, NASA's congressionally mandated replacement for the Space Shuttle. Space Vehicle Program managers often make decisions for on-orbit spacecraft without ever having on-orbit space flight experience. Intelligent equipment would have eliminated the catastrophic failures on the NASA Space Shuttle Challenger and Columbia. These accidents occurred due to the lack of space vehicle subsystem engineering personnel analyzing real-time equipment telemetry presented on strip chart and video data prior to lift off during pre-launch checkout for the Space Shuttle Challenger and the lack of space vehicle real-time equipment telemetry for Columbia. The PHM requires all equipment to include analog telemetry for measuring the equipment performance and usable life determination in real-time and a prognostic analysis completed manually will identify the equipment that will fail prematurely for replacement before launch preventing catastrophic equipment failures that may cause loss of life.

This thesis is concerned with condition monitoring and quantitative analysis of synchronous motors. Constantly rising requirements on the reliability of motors develop new methods of predictive diagnostics, which can identify failure conditions in the initial stage. Created mechatronic systems with the implemented failure from pre-prepared PMSM model are tested by unconventional condition monitoring methods. Software solutions of diagnostics and model designs of the mechatronic systems are implemented in MATLAB & Simulink. Part of this work is also a short introduction to the issue with necessary theoretical fundamentals and research of some selected methods of predictive diagnostics.

Sequences are ubiquitous in the domain of biology. One of the current best machine learning techniques for analysing sequences is the long short-term memory (LSTM) network. Owing to significant barriers to adoption in biology, focussed efforts are required to realize the use of LSTMs in practice. Thus, the aim of this work is to improve the state of LSTMs for biology, and we focus on biological tasks pertaining to physiological signals, peripheral neural signals, and molecules. This goal drives the three subplots in this thesis: biological applications, visualizations, and extensions. We start by demonstrating the utility of LSTMs for biological applications. On two new physiological-signal datasets, LSTMs were found to outperform hidden Markov models. LSTM-based models, implemented by other researchers, also constituted the majority of the best performing approaches on publicly available medical datasets. However, even if these models achieve the best performance on such datasets, their adoption will be limited if they fail to indicate when they are likely mistaken. Thus, we demonstrate on medical data that it is straightforward to use LSTMs in a Bayesian framework via dropout, providing model predictions with corresponding uncertainty estimates. Another dataset used to show the utility of LSTMs is a novel collection of peripheral neural signals. Manual labelling of this dataset is prohibitively expensive, and as a remedy, we propose a sequence-to-sequence model regularized by Wasserstein adversarial networks. The results indicate that the proposed model is able to infer which actions a subject performed based on its peripheral neural signals with reasonable accuracy. As these LSTMs achieve state-of-the-art performance on many biological datasets, one of the main concerns for their practical adoption is their interpretability. We explore various visualization techniques for LSTMs applied to continuous-valued medical time series and find that learning a mask to optimally delete information in the input provides useful interpretations. Furthermore, we find that the input features looked for by the LSTM align well with medical theory. For many applications, extensions of the LSTM can provide enhanced suitability. One such application is drug discovery -- another important aspect of biology. Deep learning can aid drug discovery by means of generative models, but they often produce invalid molecules due to their complex discrete structures. As a solution, we propose a version of active learning that leverages the sequential nature of the LSTM along with its Bayesian capabilities. This approach enables efficient learning of the grammar that governs the generation of discrete-valued sequences such as molecules. Efficiency is achieved by reducing the search space from one over sequences to one over the set of possible elements at each time step -- a much smaller space. Having demonstrated the suitability of LSTMs for biological applications, we seek a hardware efficient implementation. Given the success of the gated recurrent unit (GRU), which has two gates, a natural question is whether any of the LSTM gates are redundant. Research has shown that the forget gate is one of the most important gates in the LSTM. Hence, we propose a forget-gate-only version of the LSTM -- the JANET -- which outperforms both the LSTM and some of the best contemporary models on benchmark datasets, while also reducing computational cost.