=Paper=
{{Paper
|id=Vol-1507/dx15paper27
|storemode=property
|title=Device Health Estimation by Combining Contextual Control Information with Sensor Data
|pdfUrl=https://ceur-ws.org/Vol-1507/dx15paper27.pdf
|volume=Vol-1507
|dblpUrl=https://dblp.org/rec/conf/safeprocess/HondaLESAPI15
}}
==Device Health Estimation by Combining Contextual Control Information with Sensor Data==
https://ceur-ws.org/Vol-1507/dx15paper27.pdf
Proceedings of the 26th International Workshop on Principles of Diagnosis
Device Health Estimation by Combining
Contextual Control Information with Sensor Data
Tomonori Honda and Linxia Liao and Hoda Eldardiry and Bhaskar Saha and Rui Abreu
Palo Alto Research Center, Palo Alto, California, USA
e-mail: {tomo.honda, linxia.liao, hoda.eldardiry, bhaskar.saha, rui.maranhao}@parc.com
Radu Pavel and Jonathan C. Iverson
TechSolve, Inc., Cincinnati, Ohio, USA
e-mail: {pavel, iverson}@TechSolve.org
Abstract discusses various categories of anomaly detection technolo-
gies and their assumptions as well as their computational
The goal of this work is to bridge the gap between complexity. Several approaches such as statistical meth-
business decision making and real-time factory ods [3], neural network methods [4] and reliability meth-
data. Beyond real-time data collection, we aim to ods [5], have been applied to detect anomalies for various
provide analysis capability to obtain insights from types of equipment. The philosophies and techniques of
the data and converting the learnings into action- monitoring and predicting machine health with the goal of
able recommendations. We focus on analyzing improving reliability and reducing unscheduled downtime
device health conditions and propose a data fusion of rotary machines are presented by Lee et al. [6].
method that combines sensor data with limited di-
agnostic signals with the device’s operating con- Many of these methods focus on analyzing, combining,
text. We propose a segmentation algorithm that and modeling sensor data (e.g. vibration, current, acous-
provides a temporal representation of the device’s tics signal) to detect machine faults. One issue that remains
operation context, which is combined with sensor mostly unaddressed in these methods is that they rarely con-
data to facilitate device health estimation. Sensor sider the varying operating context of the machine. In many
data is decomposed into features by time-domain cases, false alarms are generated due to a change in machine
and frequency-domain analysis. Principal com- operation (e.g. rotational speed) rather than a change in ma-
ponent analysis (PCA) is used to project the high- chine condition. A major challenge in addressing this issue
dimensional feature space into a low-dimensional is that most machine controllers are built with proprietary
space followed by a linear discriminant analysis communication protocols, which leads to a barrier in ob-
(LDA) to search the optimal separation among taining control parameters to understand the context under
different device health conditions. Our industrial which the machine is operating. Recently, the MTConnect
experimental results show that by combining de- open protocol [7] was developed to connect various legacy
vice operating context with sensor data, our pro- machines independent of the controller providers. MTCon-
posed segmentation and PCA-LDA approach can nect provides an unprecedented opportunity to monitor ma-
accurately identify various device imbalance con- chine operating context in real-time. In this paper, we lever-
ditions even for limited sensor data which could age MTConnect to diagnose machine health condition by
not be used to diagnose imbalance on its own. combining sensor data with operating context information.
Additionally, we investigate whether it is possible to diag-
nose machine health condition using less sensor data when
1 Introduction it is combined with context information.
The growing Internet of Things is predicted to connect 30 Prior work [8] has demonstrated that vibration data could
billion devices by 2020 [1]. This will bring in tremendous be used for diagnosing machine imbalance fault conditions.
amounts of data and drive the innovations needed to realize Our study focuses on extending prior work by exploring var-
the vision of Industry 4.0—cyber-physical systems moni- ious types of sensor and control data for diagnosing the im-
toring physical processes, and communicating and cooper- balance of the machine tools.
ating with each other and with humans in real time. One of
the key challenges to be addressed is how to analyze large Our contribution includes the following extensions:
amounts of data to provide useful and actionable informa-
tion for businesses intelligence and decision making. In par- • Combining control and sensor signals to improve ac-
ticular, to prevent unexpected downtime and its significant curacy.
impact on overall equipment effectiveness (OEE) and total
cost of ownership (TCO) in many industries. Continuous • Utilizing a different set of sensor data such as temper-
monitoring of equipment and early detection of incipient ature, power, flow, and lubricant/coolant pH.
faults can support optimal maintenance strategies, prevent
downtime, increase productivity, and reduce costs. Our hypothesis is that these advancements to prior work
A significant number of anomaly detection and diagno- will aid in improving the diagnosis capability as well as re-
sis methods have been proposed for machine fault detection ducing the cost of machine diagnostics by utilizing cheaper
and machine health condition estimation. Chandola et al. [2] sensors.
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Proceedings of the 26th International Workshop on Principles of Diagnosis
2 Experimental Data sufficient degree. Additionally, if the associated sensors are
The data under study has been collected from experiments too expensive to install, then data fusion may be applied.
utilizing a machine tool monitoring system implemented on There are three data fusion approaches typically used in
a horizontal machining center manufactured by Milltronic machinery diagnostics [10; 11]—data-level fusion, feature-
with Fanuc 0i-MC control. We have two main sources of level fusion, and decision-level fusion. Data-level fusion
data: (i) data from additional sensors installed on the ma- involves combining sensor data before feature extraction,
chine, and (ii) data from the machine tool controller. This such that features contain information gathered from mul-
data has been collected using National Instrument equip- tiple sensors. Feature-level fusion involves generating fea-
ment and software (LabVIEW). tures from each sensor separately, then fusing this set of
The external sensors used for data collection include: features generated from all of the sensors coherently for di-
agnostics. Finally, decision-level fusion creates diagnostics
• power sensor that measures power using Hall effect, from each sensor separately, then aggregates these diagnos-
• accelerometers that capture machine tool motion in 6 tics into a single diagnostic output.
degrees of freedom, The choice of the three types of data fusion methods is
often application specific. In our application, we found that
• thermocouples that measure temperatures at 10 loca-
temperature sensor data cannot resolve imbalance condi-
tions on the machine tool,
tions by itself and control signal data is too coarse-grained
• pH sensor for detecting the pH level of the metalwork- to aid in classifying imbalance conditions using the stan-
ing fluid, and dard data-fusion techniques. Note that we did not focus on
• flow rate sensor to measure metalworking fluid pump spindle acceleration data, which could diagnose imbalance
flow. on its own (see Subsection 4.1) since that would require
retrofitting existing machine tools with new expensive sen-
The second category consists of data collected from the sors and data acquisition hardware. Ideally we would like
controller. This data includes drive loads, absolute and rel- to use the readily accessible control signals and data from
ative positions, servo delays, and feed rate. The complete inexpensive temperature sensors to diagnose imbalance. To
list of the components of the control data is listed in Pavel achieve this goal, we proposed a different type of data fusion
et al.[8]. approach. We used the control signal to provide the contex-
Data has been collected in two sessions, one in 2009 and tual information for temperature sensor data. The control
the other in 2010. Although the basic control signals are signal is used for the segmentation of sensor data, but does
similar, they are offset by constant values (see Figure 1). not directly map into feature vectors (see Subsection 4.2).
Since the positional offset could cause a difference in the
motion dynamics, we have treated them as separate data sets 3.2 Model Selection
for this study.
Since the data sets are statistically small and dimensional-
ity of the data is increased by feature synthesis, the models
3 Technical Approach to be used for imbalance classification need to be carefully
chosen to avoid over-fitting. The high-dimensional data
For each extension to prior work listed in Section1, we needs to be projected to a much smaller sub-space to prevent
have performed two main steps for creating appropriate di- over-fitting1 To accomplish this, the main techniques used
agnostics: in this study are Principal Component Analysis (PCA) [12]
• Feature Extraction & Synthesis and Linear Discriminant Analysis (LDA) [13]. These tech-
niques are based on linear coordinate transformation, which
• Model Selection makes them more likely to under-fit and less likely to over-
fit [14].
3.1 Feature Extraction & Synthesis
There are various approaches for condensing time series in- 4 Results
formation into data mining features. Prior work has utilized
transfer functions to map control signals to vibrational sen- We have explored three types of imbalance diagnostics to
sor data [8]. The diagnosis step is then reduced to compar- investigate the hypothesis posed in Section 1:
ing the features of transfer function-predicted vibration data • Sensor based Diagnostics
and the sensor-derived vibration data. This approach makes
• Control based Temporal Segmentation followed by
sense when the control signal directly impacts the output
Sensor based Diagnostics
variables of the machine. For motion control of machine
tools, the estimated transfer function should be similar to 4.1 Sensor based Diagnostics
the transfer function of the implemented control (like PI or
PID). Typical vibration data features would include average, In this case, each sensor signal was analyzed separately to
standard deviation, and maximum FFT values [9]. determine if any of the sensor signals contains enough diag-
However, we would like to diagnose the state of machine nostic information to detect imbalance on its own. By plot-
using not only accelerometers, but also other sensors, such ting the time series data we find that spindle acceleration
as temperature sensors. Since temperatures at various lo- sensors (which captures vibration) show higher oscillation
cations are not part of active control loops, there may not 1
Note that complexity of model is positively correlated with
exist well defined transfer functions that can map control likelihood of over-fitting. Thus, creating a classifier that takes
signals to temperature sensor data very accurately. In such high-dimensional input will have higher degree of fredoom (i.e.
cases where conventional features extracted from tempera- higher complexity) compare to low-dimensional inputs, which re-
ture signals are not correlated with the fault (imbalance) to a sults in higher likelihood of over-fitting.
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Proceedings of the 26th International Workshop on Principles of Diagnosis
(a) Absolute X position (b) Absolute Y position
(c) Absolute Z position (d) Spindle Motor Speed
Figure 1: Primary Control Signals
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Proceedings of the 26th International Workshop on Principles of Diagnosis
amplitudes (see Figure 2) with increasing imbalance. Since computed the standard deviation at the each time step and
imbalance actually impacts moment of inertia of the spindle, identified the periods with standard deviation below a set
this change in acceleration is expected. threshold to find the consistent time intervals (shown as col-
We also considered measuring imbalance through tem- ored segments along the time axis in Figure 5 (b)). Then
perature. From the energy flow perspective, additional ac- we find the intersection of the sets of consistent time inter-
celeration caused by imbalance should result in higher en- vals over all the control signals to determine the aggregate
ergy consumption from the power source and higher energy time intervals over which the control signals are statistically
dissipation to thermal inertias due to friction, which should consistent (shown as black segments along the time axis in
result in temperature increase in parts of the machine tool. Figure 5 (c)).
However, the time series data, from each of the tempera- These temporal segments are then mapped to sensor data
ture sensors, did not show distinguishing features similar to to facilitate diagnostics. For each of 16 temporal segments,
the acceleration sensors. An example of temperature sensor we computed features including (i) average, (ii) standard de-
time series data is shown in Figure 3. viation, (iii) maximum FFT value, and (iv) FFT frequency
at maximum amplitude. This step produces a 64 dimen-
sional feature space to diagnose machine imbalance. As
mentioned before, to avoid the overfitting we focus on linear
transformation based approaches. We implemented Princi-
pal Component Analysis (PCA) to reduce the dimensional-
ity from 64 to 4 (postulating that there should be 4 unique
dimensions given the 4 uncorrelated features that we have
selected). The PCA step is followed by Linear Discrimi-
nant Analysis to find the optimal coordinate transformation
that provides maximum separation between classes. Result
of this PCA-LDA analysis is shown in Figure 6 for Fluid
Temperature sensor data. Another temperature sensor lo-
cated at Spindle Motor also exhibits similar diagnostic ca-
pability after application of control based temporal segmen-
tation. This demonstrates that control data can be used to
provide context to sensor data in a way that helps diagnose
machine imbalance. Thus, temperature sensor which had
Figure 3: Sample Temperature Sensor Data (Fluid Temper- inferior diagnostic performance without context data, could
ature): blue and red traces indicate nominal and faulty con- classify imbalance perfectly when it is combined with addi-
ditions respectively tional context from control signal.
For this sensor data analysis, the features extracted are (i)
5 Conclusion and Discussion
average, (ii) standard deviation, (iii) maximum amplitude This work explores various types of sensor and control data
of FFT, and (iv) frequency for maximum amplitude of FFT. for diagnosing the imbalance of the machine tools. Our
These four features are inspected visually to determine if proposed approaches utilize sensor data that has not been
imbalance could be classified by a simple linear classifier. used before for this purpose. This includes temperature,
The spindle acceleration (X, Y, and Z) feature (maximum power, flow, and lubricant/coolant pH. In addition, our pro-
amplitude of FFT) showed easily visible characteristics that posed techniques combine control and sensor signals to im-
can distinguish between degrees of imbalance. See Figure 4 prove accuracy. Namely, by combining context information
for an example of visual classification based on X-axis ac- gained from the control signal, temperature sensor was able
celeration data. Other sensor signals like power, pH, flow, to classify machine imbalance conditions with much higher
and temperature did not exhibit such classification capabil- accuracy than using itself alone.
ity. For future work, we will explore diagnostics based on
control signal alone. Given that relying on sensor data typ-
4.2 Control-based Segmentation followed by ically requires adding sensors to existing machine tools, it
Sensor-based Diagnostics would be ideal if we could diagnose imbalance of the ma-
chine from control signals that are usually recorded (i.e. no
The second diagnostic approach that we explored combines additional hardware required). The expectation is that if a
both sensor and control data in a coherent manner. The machine tool uses feedback controls, then the control signal
first step in this approach is to utilize the control signal to should be impacted by any change in the operational char-
provide temporal segmentation, i.e., assuming quasi-steady acteristics (in this case the imbalance of the machine tools).
state, the goal is to find the time intervals in which the fol-
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