A novel fault diagnostics and prediction scheme in continuous-time is introduced for a class of nonlinear systems. The proposed method uses a novel neural network (NN) based robust integral sign of the error (RISE) observer, or estimator, allowing for semi-global asymptotic stability in the presence of NN approximation errors, disturbances and unmodeled dynamics. This is in comparison to typical results presented in the literature that show only boundedness in the presence of uncertainties. The output of the observer/estimator is compared with that of the nonlinear system and a residual is used for declaring the presence of a fault when the residual exceeds a user defined threshold. The NN weights are tuned online with no offline tuning phase. The output of the RISE observer is utilized for diagnostics. Additionally, a method for time-to-failure (TTF) prediction, a first step in prognostics, is developed by projecting the developed parameter-update law under the assumption that the nonlinear system satisfies a linear-in-the-parameters (LIP) assumption. The TTF method uses known critical values of a system to predict when an estimated parameter will reach a known failure threshold. The performance of the NN/RISE observer system is evaluated on a nonlinear system and a simply supported beam finite element analysis (FEA) simulation based on laboratory experiments. Results show that the proposed method provides as much as 25% increased accuracy while the TTF scheme renders a more accurate prediction.

In this work, an inexpensive electric utilities monitoring solution using wireless sensor networks is demonstrated that can easily be installed, deployed, maintained and eliminate unnecessary energy costs and effort. The monitoring solution is designed to support network enabled manufacturing (NEM) program using Missouri University of Science and Technology (MST), formerly the University of Missouri-Rolla (UMR), motes.

A novel adaptive distributed rate and power control (ADRPC) protocol is introduced for wireless networks. The proposed controller contrasts from others by providing nonlinear compensation to the problem of transmission power and bit-rate adaptation. The protocol provides control of both signal-to-interference ratio (SIR) and quality-of-service (QoS) support to bit-rate adaptation. Bit-rate adaptation is performed by local estimation of congestion levels, rendering little packet overhead, using Lyapunov based adaptive control methods. Performance of the proposed control scheme is shown through analytical proof and simulation examples.

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A novel adaptive and distributed fair scheduling (ADFS) scheme for wireless sensor networks is shown through hardware implementation. In contrast to simulation, hardware evaluation provides valuable feedback to protocol and hardware development process. The proposed protocol focuses on quality-of-service (QoS) issues to address flow prioritization. Thus, when nodes access a shared channel, the proposed ADFS allocates the channel bandwidth proportionally to the weight, or priority, of the packet flows. Moreover, ADFS allows for dynamic allocation of network resources with little added overhead. Weights are initially assigned using user specified QoS criteria. These weights are subsequently updated as a function of delay, enqueued packets, flow arrival rate, and the previous packet weight. The back-off interval is also altered using the weight update equation. The weight update and the back-off interval selection ensure that global fairness is attained even with variable service rates. The algorithm is implemented using UMR/SLU motes for an industrial monitoring application. Results the hardware implementation demonstrates improved performance in terms of fairness index, flow rate, and delay.