Robotic exoskeletons are promising devices capable of both administering therapeutic exercises and assessing human movement quality. Although assessing fatigue is crucial to informing effective strategies for rehabilitation, existing metrics for evaluating fatigue during robot-mediated exercise remain underdeveloped. Current techniques focus on monitoring localized muscle fatigue, but do not consider the complex relationship between changes in muscle activity and associated alterations in joint motion during dynamic movement. In this work, we propose a system-based monitoring paradigm for tracking fatigue-induced changes in performance. The method uses a time-series model to approximate the dynamics of a human-exoskeleton system by mapping muscle activity to movement variables. An index of performance is calculated from modeling errors to continuously track changes in this dynamic relationship over time. Results showed that the index effectively captured fatigue-induced degradation in performance over time during an exoskeleton-administered resistive exercise. The index outperformed a traditional indicator of fatigue that is typically used during robotic intervention, suggesting the proposed approach has the potential to improve fatigue monitoring efforts during robot-aided movement training.

In this paper, we propose a Bayesian estimation scheme for hidden Markov model (HMM) parameters, as well as a method for monitoring systems whose degradation processes are modeled using HMMs identified using this novel estimation approach. The Bayesian estimation naturally yields information about model parametric uncertainties via posterior distributions of HMM parameters emanating from the estimation procedure. Numerous simulations implementing the newly proposed method for estimation of HMM parameters were conducted demonstrating its capability to identify several types of HMMs, including the commonly encountered ergodic and homogeneous HMM, as well as less commonly studied nonergodic or/and nonhomogeneous HMM. In addition, a novel condition monitoring scheme based on uncertain HMMs of the degradation process is proposed and demonstrated on a large dataset obtained from a semiconductor-manufacturing facility. A small portion of the data was used to build operating mode specific HMMs of machine degradation via the newly proposed Bayesian estimation, while the remainder of the data was used for condition monitoring based on the uncertain degradation HMMs yielded by the novel Bayesian estimation method. Comparison with a traditional sensory signature-based statistical monitoring method showed that the newly proposed approach significantly outperforms the traditional method in terms of its detection capabilities and false alarm ratios.

The self-organizing network (SON)-based multiple model system is a recently proposed method for identifying the dynamics of a general nonlinear system. It has been observed by researchers that cooperative learning among neighboring regions is sometimes important for the success of identification of a nonlinear system under the multiple model system framework. In this paper, we intend to formally evaluate the effects of cooperative learning and topology preservation in identification of multiple model system based on SON. The results of the mathematical analysis supports the heuristic that a good learning strategy for identifying the local model parameters of a SON-based multiple model system is to choose a neighborhood function whose effective region is initially wider and is reduced gradually during learning. An example of nonlinear function approximation is also provided at the end of this paper to demonstrate the results of the mathematical analysis.

As more electronic devices are integrated into automobiles to improve the reliability, drivability and maintainability, automotive diagnosis becomes increasingly difficult to deal with. Unavoidable design defects, quality variations in the production process as well as different usage patterns make it is infeasible to foresee all possible faults that may occur to the vehicle. As a result, many systems rely on limited diagnostic coverage provided by a diagnostic strategy which tests only for a priori known or anticipated failures, and presumes the system is operating normally if the full set of tests is passed. To circumvent these difficulties and provide a more complete coverage for detection of any fault, a new paradigm for design of automotive diagnostic systems is needed. An approach inspired by the functionalities and characteristics of natural immune system is presented and discussed in the paper. The feasibility of the newly proposed paradigm is also partially demonstrated through application examples.

Today, machining systems are complex multistation manufacturing systems that involve a large number of machining operations and several locating datum changes. Dimensional errors introduced at each machining operation get transformed and cause the occurrence of new errors as the workpiece propagates through the machining system. The appropriate choice of measurements in such a complex system is crucial for the subsequent successful identification of the root causes of machining errors hidden in dimensional measurements of the workpiece. In order to facilitate this measurement selection process, methods for quantitative characterization of measurement schemes must be developed. This problem of quantitative measurement characterization referred to as the measurement scheme analysis problem is dealt with in this paper. The measurement scheme analysis is accomplished through characterization of the maximal achievable accuracy of estimation of process-level parameters based on the measurements in a given measurement scheme. The stream of variation methodology is employed to establish a connection between the process-level parameters and measured product quality. Both the Bayesian and non-Bayesian assumptions in the estimation are considered and several analytical properties are derived. The properties of the newly derived measurement scheme analysis methods are demonstrated in measurement scheme characterization in the multistation machining system used for machining of an automotive cylinder head. Note to Practitioners-Techniques for the measurement scheme analysis presented in this paper enable one to formally and systematically evaluate the amount of information content a given combination of measurements carries about the process-level faults that cause quality problems in machining. Such techniques can be used to optimally select measurements that are the most informative about the machining process before a machining line is even built. Since the basis of the measurement scheme analysis methods presented in this paper is the model of the flow of machining errors referred to as the stream-of-variation (SoV) model, which can currently model only the influence of fixture and tool path parameter errors on the workpiece quality, the results presented in this paper only evaluate the amount of information about those error sources. However, as improvements to the SoV models are made in the future, the measurement scheme analysis methods and their properties demonstrated in this paper will remain valid as long as the newly obtained modes are in the linear state-space form possessed by the existing SoV models