Mr. Rollin Lakis Profile

Rollin Lakis

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 6 October 2023 Poster

Unsupervised methods and ensemble learning to classify vibration sensor data

Emily Teti, Rollin Lakis, Vlad Henzl

Proceedings Volume 12675, 1267511 (2023) https://doi.org/10.1117/12.2677375

KEYWORDS: Sensors, Vibration, Data processing, Binary data

Read Abstract +

We use unsupervised machine learning approaches in a completely data-driven binarization routine for vibration sensors with minimal lag. Gyroscopic vibration sensors are inherently noisy as they report analog signals that must be translated into digital values, in our case whether a pump is running (“on”) or not (“off”). When analyzing data from different pumps, each of which has their own baseline vibration values and magnitude of vibration, manual annotation is not feasible. We have tested multiple unsupervised methods including k-means clustering, Gaussian naïve Bayes, and ensemble learning to correctly binarize the analog signals. Comparisons are made on the basis of “blips” or times where the algorithm predicted an incorrect state for a short period of time before returning to the current state. This provides an objective metric with which to evaluate an algorithm’s success at binarizing the signal. We present results from an experimental design to probe the efficacy of different learning methods across data collected from ten vibration sensors deployed on pumps in a water treatment plant. We use initial k-means clustering for many algorithms to get an initial guess of the on or off state of the pump. From there we use a variety of smoothing, Gaussian naïve Bayes classifiers, and ensemble learning to get a final classification of pump activity. We apply these methods to data collected from ten sensors deployed on distinct pumps.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years