Shipboard Fault Detection Through Nonintrusive Load Monitoring: A Case Study

Abstract

As crew sizes aboard maritime vessels shrink in efforts to reduce operational costs, ship operators increasingly rely on advanced monitoring systems to ensure proper operation of shipboard equipment. The nonintrusive load monitor (NILM) is an inexpensive, robust, and easy to install system useful for this task. NILMs measure power data at centralized locations in ship electric grids and disaggregate power draws of individual electric loads. This data contains information related to the health of shipboard equipment. We present a NILM-based framework for performing fault detection and isolation, with a particular emphasis on systems employing closed-loop hysteresis control. Such controllers can mask component faults, eventually leading to damaging system failure. The NILM system uses a neural network for load disaggregation and calculates operational metrics related to machinery health. We demonstrate the framework’s effectiveness using data collected from two NILMs installed aboard a U.S. Coast Guard cutter. The NILMs accurately disaggregate loads, and the diagnostic metrics provide easy distinction of several faults in the gray water disposal system. Early detection of such faults prevents costly wear and avoids catastrophic failures.