(625b) A Multi-Sensor Error Detection and Functional Redundancy Algorithm for Dynamic Systems

A
Multi-Sensor Error Detection and Functional Redundancy Algorithm for Dynamic Systems

Jianyuan
Feng ⁽¹⁾, Iman Hajizadeh⁽¹⁾, Sediqeh Samadi⁽¹⁾,
Mert Sevil⁽²⁾,
Nicole
Frantz⁽²⁾, Rachel Brandt⁽²⁾, Caterina Lazaro⁽³⁾,
Zacharie Maloney⁽³⁾, Xia Yu⁽⁴⁾, Elizabeth Littlejohn⁽⁵⁾,
Laurie Quinn⁽⁶⁾, Ali Cinar^(1,2)

⁽¹⁾ Department of Chemical and
Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA

⁽²⁾ Department of Biomedical
Engineering, Illinois Institute of Technology, Chicago, IL, USA

⁽³⁾ Department of Computer
Engineering, Illinois Institute of Technology, Chicago, IL, USA

⁽⁴⁾ Department of Control Theory and
Control Engineering, Northeastern University, Shenyang, Liaoning China

⁽⁵⁾ Department of Pediatrics,
University of Chicago, Chicago, IL, USA

     ⁽⁶⁾ College of Nursing, University
of Illinois at Chicago, Chicago, IL, USA

Abstract:

Sensor errors degrade the
performance of supervision and control systems. Sensor accuracy can be affected
by many factors such as extreme working conditions, sensor deteriorations and interferences
from other devices. Sensor error such as missing signals may disrupt system
operation and signal bias may cause inaccurate manipulate variable calculation by
the controller that may threaten the productivity and safety of the system. For
a dynamic system, it may be difficult to distinguish between some sensor errors
and real dynamic changes in the system. Many systems may have multiple sensors
and the signals from those sensors may be correlated with each other.  This offers
the opportunity for using the redundancy to detect sensor errors and reconcile
erroneous signals.

A hybrid online multi-sensor
error detection and functional redundancy (SED&FR) system is developed to
monitor the performance of multiple sensors and reconcile the erroneous signal
values. The SED&FR system relies on two technologies, outlier-robust Kalman
filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression
model. The two methods have their unique way of using data, ORKF is comparing
current signal samples with signal trace indicated by previous samples and
LW-PLS is comparing previous samples with the samples from a database and use
the most similar samples to build a model to predict current signal values.
Sensor errors such as outliers can be detected more easily by ORKF. Long-duration
sensor error detection and reconciliation can be handled by LW-PLS which relies
on the prediction ability based on historical data sets. SED&FR leverages
the advantages of automatic measurement error elimination with ORKF and
data-driven prediction with LW-PLS.

The performance of this system is
illustrated with a clinical case study which collects data from a continuous
glucose monitoring (CGM) system and a wearable device that provides energy
expenditure (EE) and Galvanic Skin Response (GSR) information from people with
type 1 diabetes. More than 10,000 signal sets are used for testing. The results
indicate that the proposed system can successfully detect most of the erroneous
signals and substitute them with reasonable estimated values computed by
functional redundancy system.