George Lederman

Signal inpainting on graphs via total variation minimization

We propose a novel recovery algorithm for signals with complex, irregular structure that is commonly represented by graphs. Our approach is a generalization of the signal inpainting technique from classical signal processing. We formulate corresponding minimization problems and demonstrate that in many cases they have closed-form solutions. We discuss a relation of the proposed approach to regression, provide an upper bound on the error for our algorithm and compare the proposed technique with other existing algorithms on real-world datasets.

Rail-infrastructure Monitoring through the Dynamic Response of a Passing Train

We present a system for monitoring rail infrastructure from the dynamic response of a train in revenue-service. Presently, rail inspection is performed either visually or with dedicated track vehicles. We propose monitoring the tracks from operational trains, using accelerometers inside the cabin of the train. This would offer an economical approach, as accelerometers are a low-cost technology, tracks would not have to be taken out of service for inspection, and no dedicated inspection vehicles or crews would be required. However, using operational trains presents three challenges: the speed of the train varies from run to run, the position of the train is not known with high accuracy, and the vibration signal recorded in the train’s cabin is filtered by the train and its suspension. We propose extracting features from the signal which are robust to these conditions and show that our proposed features allow for detecting changes in the infrastructure from the train’s dynamic response. We demonstrate our ability to detect subtle track changes both through simulation and using data we collected from Pittsburgh’s Light Rail system. This type of sensing, signal processing, and data analysis could facilitate safer trains and more cost-efficient maintenance in the future. doi: 10.12783/SHM2015/182

STIM: smart train infrastructure monitoring

Globally, infrastructure is a vital asset for economic prosperity, but condition assessments tend to be subjective and infrequent [2]. The lack of objective information leads to sub-optimal capital replacement projects, and the information lag prevents timely repair. In this poster we focus on techniques for monitoring rail-based transit infrastructure, although many of the findings could easily be applied in other types of infrastructure. One monitoring solution is to instrument the tracks and track structures, but given the expanse of our transit networks, the installation and maintenance cost of such a sensor network would be prohibitively high. A second solution is to use a custom instrumentation vehicle capable of monitoring the infrastructure as it moves [1]. However, such dedicated vehicles tend to be expensive, particularly in rail where monitoring is more specialized, so to keep costs down, infrastructure owners use these vehicles infrequently.

Mitigating the effects of variable speed on drive-by infrastructure monitoring

Vehicle-based monitoring has the potential to become an accurate and cost-efficient way to monitor infrastructure assets, but a number of challenges must be addressed for such a technique to be implemented widely. The majority of vehicle-based infrastructure sensing has assumed that the vehicle’s speed profile is identical every time it passes over the asset of interest. Ultimately, however this technology will be most practical if damage detection schemes can be applied regardless of the speed of the vehicle. Thus methods must be designed to handle speed variability to make this method more practical. In this paper we investigate the effects of variable speed when monitoring infrastructure from the dynamic response of a passing vehicle, which we measure by placing accelerometers on the vehicle of interest. We have conducted a series of laboratory tests to study this phenomenon, in which a vehicle crosses over a scaled model bridge structure with a varying speed profile. We quantify the ability of several features to detect changes in the infrastructure, independent of the variable speed. We show that aligning signals to normalize for speed variability improves the classification results. This work brings us closer to the ultimate goal of using vehicle-based monitoring to ensure more efficient and more reliable infrastructure in the future.

Remote Placement of Magnetically Coupled Ultrasonic Sensors for Structural Health Monitoring

In this work we develop an intelligent remote sensor placement system for standoff deployment of magnetically coupled ultrasonic sensors for structural health monitoring applications. Currently there exists significant legacy infrastructure that requires monitoring. Sensors often need to be accurately placed in hard-to-reach locations which are exposed to harsh environmental conditions, all while ensuring adequate mechanical coupling between the sensor and the structure. Installing these sensors is a task which is time consuming, expensive, and dangerous. In this paper, we develop an intelligent pneumatic remote sensor placement system meant to be integrated with commercially available multicopters. It is designed to accurately deploy sensor nodes from a standoff distance. To achieve this it will calculate the required trajectory and energy requirements to ensure proper placemen as well as coupling between the node and the structure without damaging the sensor package or the structure in the process. This work leverages recent advances in computer vision and commercially available multicopters to align the remote sensor placement system with the point of attachment on the structure. This technology will reduce the barriers associated with the deployment of large scale sensor networks in the field of structural health monitoring.