Novel adaptive management system boosts efficiency of wireless sensor networks that monitor surrounding environments

Mar 14, 2013

Electronic engineers in Singapore have developed and successfully tested a management system that increases the efficiency of wireless sensor networks for monitoring machine health. The new system, known as an adaptive classification system (ACS), reduces the power consumption of individual sensors and increases their lifespan, while also decreasing network traffic and data storage requirements.

The ACS also achieves more robust results in terms of diagnosis of machine problems and prognosis of performance. "Other applications include monitoring patient health, disaster , such as fire alarms, and environmental monitoring for chemical plant accidents, air and water quality," says Minh Nhut Nguyen of the A*STAR Institute for Infocomm Research, who led the research team.

Wireless sensors are now so inexpensive and flexible that their application in monitoring systems is widespread. Because of the environments in which they are deployed, sensors increasingly require their own portable power source, typically a battery, which means they have a limited lifespan. Any way of reducing the amount of power the sensors draw would increase their lifespan, decrease the need to replace them and therefore reduce costs, Nguyen explains.

Reducing sensor sampling rates to a practical minimum is one way to ; this can be achieved by halting monitoring when a machine is not operating. Typically, a machine functioning smoothly demands a lower and coarser sampling rate than one that needs attention. Nguyen and his coworkers therefore developed their ACS along these lines.

Importantly, it incorporates an of nested sensors. Some of the ACS sensors sample particular parameters at a low rate to provide data for a model whose purpose is simply to trigger more intensive sampling of other sensors when a potential problem is detected.

In addition, the system utilizes a set of models that is geared to sensors sampling at a particular rate. The ACS also integrates several different methods of classifying whether particular data patterns are of concern such that they require higher levels of sampling. Decisions are therefore made on the basis of multiple classifications. This not only increases the robustness of the system, but also means that it can be trained to detect problems using a minimal amount of data.

Nguyen and his team tested the ACS using a machinery fault simulator, a machine in which key components, such as bearings, could be replaced by faulty or worn ones. Encouragingly, on average the ACS outperformed current models in these tests.

Explore further: Entrepreneur builds a sleek ship, but will anyone buy it?

More information: Nguyen, M. et al. Ensemble based real-time adaptive classification system for intelligent sensing machine diagnostics. IEEE Transactions on Reliability 61, 303–313 (2012). ieeexplore.ieee.org/xpl/login.… tp=&arnumber=6198746

add to favorites email to friend print save as pdf

Related Stories

Wireless patients

May 26, 2010

A wireless monitoring system for people with debilitating conditions such as Parkinson's disease or chronic obstructive pulmonary disorder (COPD) could allow healthcare workers to assess a patient's health and the development ...

Greener disaster alerts

Jun 27, 2011

New software allows wireless sensor networks to run at much lower energy, according to researchers writing in the International Journal of Sensor Networks. The technology could improve efficiency for hurricane and other ...

Rainforest rehab in every sense

Jun 12, 2009

Sophisticated sensors that measure leaf wetness, soil moisture and temperature are helping rehabilitate rainforest in the Springbrook World Heritage precinct in south-east Queensland.

A digital "stethoscope" for monitoring equipment

Jul 20, 2012

An intelligent diagnostic system from Siemens can monitor the condition of mechanical equipment just by analyzing the noises it makes. The system's sensors listen to machine noises in the same way that a doctor ...

Wireless sensors learn from life

Aug 25, 2008

(PhysOrg.com) -- European and Indian researchers are applying principles learned from living organisms to design self-organising networks of wireless sensors suitable for a wide range of environmental monitoring purposes.

Wireless nano sensors could save bridges, buildings

Apr 09, 2010

Could inexpensive wireless sensors based on nanotechnology be used to alert engineers to problematic cracks and damage to buildings, bridges, and other structures before they become critical? A feasibility study published ...

Recommended for you

Off-world manufacturing is a go with space printer

Dec 20, 2014

On Friday, the BBC reported on a NASA email exchange with a space station which involved astronauts on the International Space Station using their 3-D printer to make a wrench from instructions sent up in ...

First drone in Nevada test program crashes in demo

Dec 19, 2014

A drone testing program in Nevada is off to a bumpy start after the first unmanned aircraft authorized to fly without Federal Aviation Administration supervision crashed during a ceremony in Boulder City.

User comments : 0

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.