Pressurized rail tank cars transport large volumes of volatile liquids and gases throughout the country, much of which is
hazardous and/or flammable. These gases, once released in the atmosphere, can wreak havoc with the environment and
local populations. We developed a system which can non-intrusively and non-invasively detect and locate pinhole-sized
leaks in pressurized rail tank cars using acoustic sensors. The sound waves from a leak are produced by
turbulence from the gas leaking to the atmosphere. For example, a 500 μm hole in an air tank pressurized to 689 kPa
produces a broad audio frequency spectrum with a peak near 40 kHz. This signal is detectable at 10 meters with a
sound pressure level of 25 dB. We are able to locate a leak source using triangulation techniques. The prototype of the
system consists of a network of acoustic sensors and is located approximately 10 meters from the center of the rail-line.
The prototype has two types of acoustic sensors, each with different narrow frequency response band: 40 kHz and 80
kHz. The prototype is connected to the Internet using WiFi (802.11g) transceiver and can be remotely operated from
anywhere in the world. The paper discusses the construction, operation and performance of the system.
Notch filters are used in many industrial applications to attenuate undesired frequencies within signals. Such undesirable frequencies are common in flexible dynamic systems, power plants, medical monitoring systems, etc. In many aerospace flexible dynamic systems the desired center frequency shifts due to the nonlinearities and coupling of the system. The conventional approach in aerospace is to generate a large database filled with filter coefficients. This requires a significant verification and validation activity, as well as a large storage capacity for the filter coefficients. In this paper a model-based approach is used to design a notch filter system for a multivariable nonlinear system. Disturbance Accommodation Control ideas are presented and applied to notch filter design.
Tuning rules for adaptive neural networks have featured Lyapunov-based approaches in recent years. Although these have some desirable qualities they have led to complex tuning procedures. In order to take more advantage of the power of adaptive neural networks less complex and computationally expensive tuning rules are desirable. In addition, tuning rules should be simple and provide for rapid, reliable convergence. In this paper a proportional-integral approach to adaptive neural network tuning rules is studied. Simulation on a nonlinear system is used for a demonstration.
Flexible systems are used in many industrial designs to reduce weight and power consumption. Undesirable frequencies are common and may interfere with control systems. In many aerospace flexible dynamic systems the interfering frequency shifts due to the nonlinearities and coupling within the system. The conventional approach in aerospace is to generate a large number of individual notch filters to protect the control systems. This requires a significant verification and validation activity, as well as a large storage capacity for the filter coefficients. In this paper an MRAN neural network system is used to control a multivariable linearized space structure. Growth and pruning ideas are reviewed and applied to the space structure model. Proportional integral (PI) and lead-lag update rules are compared to a typical update rule.