Retrofittable Machine Condition and Structural Excitation Monitoring From the Terminal Box

Schantz, Christopher; Gerhard, Katie; Donnal, John; Moon, Jinyeong; Sievenpiper, Bartholomew; Leeb, Steven B.; Thomas, Kévin

doi:10.1109/jsen.2015.2498626

Cited by 8 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve all noise, vibrations and harshness (NVH) problems and avoid inefficiently optimized machines and higher operational and maintenance costs, it is necessary to have insights into the behavior of machines during their overall lifetime and for each important operating condition [2]. Moreover, since tonalities depend on the transfer path of vibrations through the overall structure [3], the optimization of NVH behavior leads to a reduction of the noise generated by the turbines, ensuring the respect of the legislation [4].…”

Section: Motivationmentioning

confidence: 99%

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

et al. 2019

View full text Add to dashboard Cite

Noise, vibration and harshness (NVH) problems are critical issues to be tackled for wind turbine drivetrains. Tracking the behavior of modal parameters of the machines’ fundamental modes during operation it is of high interest to validate complex simulation models. A powerful approach for this purpose is represented by operational modal analysis (OMA). This paper describes the investigation of an automated technique for continuously tracking the modes of a rotating mechanical system running in normal operating conditions. The modal estimation procedure is based on an automatic version of the pLSCF (poly-reference Least-Square Complex Frequency-Domain) algorithm. The latter is coupled with a method that automatically tracks the modal parameters along different data sets. The use of OMA on a rotating component of the wind turbine creates the need to deal with harmonics in order to satisfy one of the assumptions of OMA. For this purpose, the use of a cepstrum editing procedure is analyzed and implemented. Modal estimates obtained from an automated analysis on stand still data and normal operating conditions data are compared, to test the added value of the cepstrum editing procedure and the robustness of the method when used on real data. To illustrate and validate the implemented methodology, data acquired during a long-term monitoring campaign of a wind turbine drivetrain are used.

show abstract

Section: Motivationmentioning

confidence: 99%

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As such, ships increasingly rely on advanced monitoring systems to ensure proper operation of mechanical and electrical systems [3], [4]. These systems range from individual machine sensors measuring motor or generator shaft torque, speed, and vibration [5]- [7], to bulk electrical system measurements for power system state estimation and fault detection [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Shipboard Fault Detection Through Nonintrusive Load Monitoring: A Case Study

et al. 2018

View full text Add to dashboard Cite

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 (FDI), 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 (NN) 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 (USCG) 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.

show abstract

“…However, complicated wiring for their power supply and signal transmission makes the assembly and maintenance of the ventilation system significantly more complex. [4][5][6] Fortunately, a promising solution is using energy harvesting devices to convert wind energy from the ventilation system into electricity. [7][8][9][10][11] Current approaches are using conventional wind energy harvesters based on wind turbines with complex mechanical structures.…”

mentioning

confidence: 99%

A Hybridized Triboelectric‐Electromagnetic Nanogenerator as a Power Supply of Monitoring Sensors for the Ventilation System

Zheng

Zhou

et al. 2022

Advanced Energy Materials

View full text Add to dashboard Cite

To ensure the regular operation of the machine, there are so many decentralized monitoring sensors and control equipment used. However, complicated wiring for their power supply and signal transmission makes the assembly and maintenance of the ventilation system significantly more complex. [4][5][6] Fortunately, a promising solution is using energy harvesting devices to convert wind energy from the ventilation system into electricity. [7][8][9][10][11] Current approaches are using conventional wind energy harvesters based on wind turbines with complex mechanical structures. [12,13] However, these designs are complicated and costly, which limits them to be used conveniently in the ventilation system. [14][15][16] Triboelectric nanogenerator (TENG), based on triboelectrification and electrostatic induction, has merits of simple structure, light weight, low cost, and diverse material selection. [17][18][19][20][21] A more important feature is that the TENG is a viable technology to harvest wind energy and an applicable solution for self-powered sensing systems. [22][23][24][25][26][27][28][29] Under the same mechanical input, the TENG and the conventional electromagnetic generator (EMG)With the improvement of the airtightness of modern buildings, installing ventilation systems indoors is becoming increasingly important, which works for a long time continuously and needs to be monitored in real-time. However, the complex wiring of monitoring sensors makes assembly and maintenance more difficult. This work reports a hybridized triboelectric-electromagnetic nanogenerator (HNG) as a power supply of a self-powered wireless monitoring system for ventilation systems. The HNG integrates an air inlet cover, a stator with coils and interdigitated copper electrodes, a rotor with magnets, fluorinated ethylene propylene films, and optimized built-in wind blades. The HNG scavenges wind energy from a ventilation system and serves as a power supply for electric applications. Under the wind speed of 6.5 m s −1 in the ventilation system, the maximum stabilized voltage and maximum instantaneous current of the HNG are 177.5 V and 0.049 A, which can charge smartphones and light up a bulb. Furthermore, the HNG realizes the self-powered wireless transmission of temperature and humidity sensing nodes. This work demonstrates an effective wind energy harvester, providing an innovative strategy for monitoring the condition of the ventilation system and broadening the thoughts of prospective energy harvesting.

show abstract

Retrofittable Machine Condition and Structural Excitation Monitoring From the Terminal Box

Cited by 8 publications

References 7 publications

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

Shipboard Fault Detection Through Nonintrusive Load Monitoring: A Case Study

A Hybridized Triboelectric‐Electromagnetic Nanogenerator as a Power Supply of Monitoring Sensors for the Ventilation System

Contact Info

Product

Resources

About