Christopher Schantz

Fault detection and diagnostics for non-intrusive monitoring using motor harmonics

Harmonic analysis of motor current has been used to track the speed of motors for sensorless control. Algorithms exist that track the speed of a motor given a dedicated stator current measurement, for example [1–5]. Harmonic analysis has also been applied for diagnostic detection of electro-mechanical faults such as damaged bearings and rotor eccentricity [6–17]. This paper demonstrates the utility of harmonic analysis for fault detection and diagnostics in non-intrusive monitoring applications, where multiple loads are tracked by a sensor monitoring only the aggregate utility service. An optimization routine is implemented to maintain accuracy of speed estimation while using shorter lengths of data.

FPGA-based spectral envelope preprocessor for power monitoring and control

Smart Grid and Smart Meter initiatives seek to enable energy providers and consumers to intelligently manage their energy needs through real-time monitoring, analysis, and control. We have developed an inexpensive FPGA implementation of a spectral envelope preprocessor. This FPGA permits cost-effective and richly detailed power consumption monitoring for individual loads or collections of loads. It permits a flexible trade-off between data transmission, storage, and computation requirements in a power monitoring or control system. The information from the FPGA can be used to coordinate the operation of power electronic controls.

Utilizing Spin-Down Transients for Vibration-Based Diagnostics of Resiliently Mounted Machines

This paper presents a vibration measurement and analysis technique for use during a machines spin-down procedure. During spin-down, the machines operation covers a continuous wide frequency band, from operating speed to standstill, which allows the estimation of the machines vibration transfer function (VTF). This transfer function is rich in information for detecting and differentiating not only machinery pathologies but also problems with vibrational mounts. Utilizing a back-electromotive force sensor to infer rotor speed and a single-axis accelerometer for vibration measurements, this technique allows minimally intrusive estimation of a machines VTF. Data collected in laboratory and field tests aboard U.S. Navy ships are presented to demonstrate the usefulness of this monitoring technique.

Water Nonintrusive Load Monitoring

Resource conservation decisions require detailed consumption information. This paper presents sensors and signal processing techniques that use pipe vibration signatures to non-intrusively identify water consumption at the appliance level. The method requires as little as one easily installed vibration sensor. This method provides a no-fuss retrofit solution for detecting the operation of a buildings water consuming appliances. In addition, flow rate is nonintrusively obtained from a conventional water meter via a new, high sensitivity strap-on magnetic sensor. Together, these two sensors track load operating schedule and water consumption in a building, demonstrated here at three different field test sites.

WaterWOLF: Water Watch On Load Flow

We have developed WaterWOLF, a new electronic system and signal processing algorithm for evaluating flow components in pipes. These techniques nonintrusively convert an existing flow meter into a high resolution wireless meter for determining flow rates in real-time. These techniques exploit new tunneling magneto-resistive materials (TMR) for detecting very small magnetic fields generated by certain types of flow meters. The sensors are nonintrusive, requiring no new access to the flow stream. A pipe distribution network, e.g., for water or oil production or potentially even gas utilities, can not only deliver a commodity like water but can also serve as its own sensor for monitoring water flow and the operation of individual water consuming appliances. Continuous measurements of water consumption can be fed back to the user to help find opportunities for conservation. Additionally, high resolution flow sensing can improve leak detection accuracy. This is important for countries with significant water challenges like Kuwait.

Adaptive Zonal Protection for Ring Microgrids

In a fault situation on a microgrid with multiple sources, a ring distribution architecture permits healthy parts of the power distribution network to remain operational while isolating a fault. However, fault localization in a multi-ring power distribution system can be considerably more complex than for a radially distributed network. This paper presents a nonintrusive approach for making microgrid protection systems aware of load operating condition in order to improve fault detection, e.g., for detecting destructive high-impedance and arcing faults.

Retrofittable Machine Condition and Structural Excitation Monitoring From the Terminal Box

Retrofittable self-powered sensors for machine condition monitoring ease the burden of installation and decision-making for maintenance and acoustic performance assessment. Terminal box magnetic power harvesting sensors are nonintrusive. They require no special wiring and can simultaneously observe and correlate important variables for machine diagnostics, including vibration and speed. These correlated data can be used to detect and differentiate imbalances from failing structural mounts, among other possibilities. New hardware and algorithms are presented for enabling in situ vibration monitoring, with demonstrations on data sets from US Coast Guard vessels. A specific algorithmic focus of this paper is estimation of a machines contribution to structure-borne noise and vibration, an important consideration for ship acoustic signature.

Non-intrusive Load Monitoring For Water(WaterNILM)

Better water consumption decisions benefit from detailed use information. Easily installed non-intrusive vibration sensors provide a “no-fuss” retrofit solution for detecting the operation of water consuming appliances. The sensors measure pipe vibration, which are revealed to be a rich source of information for identifying loads. Vibration is processed to extract power spectral density based features which are classified with a clustering algorithm trained during install. The results can be used to track load operating schedule from the vibration data collected from as little as one pipe in a home. Mechanics governing the observed signals, and signal processing to extract operating information, are discussed in this paper. Field data from three different homes demonstrates the accuracy of this approach.

“Stethoscopes” for nonintrusive monitoring

This paper provides a survey of example sensors that can be implemented with nonintrusive electromagnetic measurements. Stray electric and magnetic fields exist around many important components in commercial and industrial processes. For example, power cables operate surrounded by magnetic and electric fields. Flow meters operate with cyclically-varying magnetic fields. And stray electromagnetic fields can serve as an energy source for powering sensors wirelessly. These stray fields provide remarkable opportunities for nonintrusive sensing of industrial processes. Sensed information can be used to establish monitoring in new or retrofit systems, or can be used as a backup or redundant source to verify the operation of an installed sensor network. Three different example sensors are presented in this paper for power monitoring, fluid flow tracking, and electromechanical vibration monitoring. All three sensors make use of a common set of circuits for electric and magnetic field sensing. They illustrate approaches that could be applied for many other sensing applications.

Self-Sensing Induction Motors for Condition Monitoring

Motors have been exploited as their own sensors for diagnostic and operating conditions at least, since the dawn of modern computing. Contracting systems theory offers a new level of precision in detecting small parameter and state changes in an electric machine for load fault detection and diagnostics from the motor terminals. The presented offline method successfully solves the motor inverse problem to reconstruct the characteristic instantaneous angular speed and load torque signals of the motor during periodic operation. The solution includes a motor parameter estimation step that reflects the specific temperatures and magnetic saturation of the motor during data acquisition. This identification or inversion method is suitable for induction motors driving periodic loads with and without rotor angle-dependent loading. A practical condition monitoring application is demonstrated: valve and cylinder fault detection in reciprocating compressors.