SAP Thought Leadership Paper
Predictive Maintenance
Using Predictive Maintenance to Approach
Zero Downtime
© 2015 SAP SE or an SAP affiliate company. All rights reserved.
How Predictive Analytics Makes This Possible
Table of Contents
4
Optimizing Machine Maintenance
4
Reactive Versus Predictive Maintenance
5
Predictive Modeling
6
Predictive Maintenance Use Cases
6
Transportation
6
Manufacturing and Production
7
Utilities
8
Medical Equipment
8
Data Centers and Clouds
9
Enabling Predictive Maintenance
9
SAP Predictive Analytics
12 Learn More
Paul Pallath, PhD
Chief Data Scientist and Director, SAP Advanced Analytics, Dublin
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Using Predictive Maintenance to Approach Zero Downtime
With the Internet of Things, machine-to-machine
communication, and connected systems, large volumes of
high-velocity data streams are now available that capture
the behavior of machines in real time. Companies can
employ powerful new predictive analytics to perform
modeling that enables predictive maintenance for these
assets, with the goal of achieving zero downtime.
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Using Predictive Maintenance to Approach Zero Downtime
Optimizing Machine Maintenance
REACTIVE VERSUS PREDICTIVE MAINTENANCE
Until now, machine maintenance has been either
reactive (performed when failure occurs) or based
on some heuristic (such as servicing the machine
every “n” hours of continuous operation). Parts
that fail earlier than expected can be very expensive and time consuming for businesses, because
the necessary tools, components, and people may
not be available for immediate response – resulting in machine downtime. But since such incidents
are sporadic, it is not economically feasible for a
company to stock all parts and tools and employ
resources just to have them at the ready; this
results in unnecessary costs. To counter this problem, companies perform scheduled maintenance
using either statistical concepts like mean time
to failure or other engineering practices based on
historical experiences. However, this approach
has its own challenge: parts can be replaced or
serviced too frequently, which drives up costs
unnecessarily. There are also instances where
machines fail before the scheduled planned
maintenance due to other reasons.
With recent advances in the Internet of Things,
machine-to-machine communication, and connected systems, it is possible to stream sensor
readings from machines so their health can be
monitored in real time. Due to the high velocity
and volume of data being streamed, it is not
humanly possible to check all sensor values. But
advances in predictive analytics have made it
possible to evaluate the changes in the patterns
of the sensor readings and identify the chances
of machine failure significantly in advance of the
event. It is also possible to identify the probability
of other parts failing in due course by analyzing
the historical patterns of failures, so that preventive maintenance can be planned. Capturing the
changes in patterns in the sensor feeds in real
time, and using that information to predict a possible failure and schedule a proactive maintenance strategy, is called predictive maintenance.
With the Internet of Things, machine-to-machine
communication, and connected systems, sensor
readings from machines can be streamed so their
health can be monitored in real time.
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Using Predictive Maintenance to Approach Zero Downtime
PREDICTIVE MODELING
Modeling for accurately predicting the probability
of occurrences of rare events like machine failures – events that occur very infrequently and
are very hard to detect – requires robust algorithms. Rare though they are, these events are
normally associated with very high costs, so
efforts to predict them are necessary. There are
two related aspects that make building models
for predicting rare events especially difficult.
First, due to the very fact that they are rare, the
training data does not contain many examples
of these events. Given that such events are often
complex (for example, the sensor readings at the
time of failure may not point to the cause of failure, since the failure could have been the result
of a buildup of several other events already
reported as past sensor readings), it is often
not easy to generalize from the few available
examples. Second, depending on how predictive
algorithms are configured and what underlying
cost functions are, classifiers or models might
completely ignore the rare event. Therefore,
special attention should be given to the choice
of performance metric being used in general
and, more important, for cases with unbalanced
classes (where one class of responses is disproportionately larger than the opposite class).
In addition to this, there are several other complexities: not all parts of a machine fail with the
same frequency, the type of failure can vary, and
more. Because sensor data is streamed in real
time, it can have missing values or garbage
values as well. The frequency at which each
sensor transmits data can also vary. Hence data
preparation and modeling techniques are needed
to help prepare the data and build predictive
models to address the nature of the problem and
capture the necessary information to effectively
predict the failure or breakdown of the machine.
Accurately predicting the probability of
occurrences of rare events like machine failures
requires robust algorithms.
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Using Predictive Maintenance to Approach Zero Downtime
Predictive Maintenance Use Cases
TRANSPORTATION
The cost for maintaining an aging fleet of commercial, private, or military vehicles is high, but
so is the cost of repairing relatively new vehicles
when they break down. There is a great benefit in
both cases to reducing the probability of a failure
by performing preventive repairs and extending
the useful life of the vehicle. Telemetric data
coupled with predictive techniques makes this
possible today. Telemetric data from thousands
of sensors captures the real-time status of the
various parts of a vehicle, which can be analyzed
for actionable insights to improve the vehicle’s
safety and performance. For example:
•• Vehicle manufacturers can use predictive analytics on sensor data to find the key factors that
influence the fuel efficiency and performance of
their vehicles and thus improve the engineering
design.
•• Vehicle manufacturers can use sensor data in
combination with geolocation to understand
the patterns of vehicle failure in different
regions and then strategize what services to
focus on in each region, and what spares to
stock up on to give their customers the best
service. Geolocation can be used to indicate
to a connected car driver that there are likely
potential problems with the car and it should
be serviced at the nearest station.
•• Vehicle sellers can analyze sensor data to
predict the probability of a breakdown far ahead
in time and proactively reach out to their customers to perform maintenance – keeping the
vehicles running and customer satisfaction
high.
•• For fleet owners and public transport departments, it is important to have their vehicles
running all the time. Predictive analytics on
real-time sensor data streams combined with
fleet management systems will make it possible
to plan predictive maintenance for fleet vehicles
while they are on the road. This will help ensure
that the vehicles are maintained at whatever
location they are in, avoiding significant downtime and financial losses due to breakdown.
MANUFACTURING AND PRODUCTION
In manufacturing and production industries
employing massive automatic machines, the
health of the machines has a significant impact
on the quality of the products produced. In addition, any machine downtime can lead to a delay
in the production cycle and create a significant
bottleneck in the entire production flow, resulting
in heavy costs. The profit margins of a company
can also be severely dented when machine failures result in substantial downtime.
Technological advances have made it possible to
collect huge volumes of sensor data and evaluate it
to identify anomalies in the readings. For example:
•• Predictive modeling techniques can be
employed to predict the failure of a part far
ahead in time and proactively replace the parts
at the most convenient time in the production
schedule to avoid any downtime.
•• Predictive modeling can be used to identify
the spare parts and the required tools that
need to be stocked for minimizing downtime
in manufacturing.
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Using Predictive Maintenance to Approach Zero Downtime
•• During the phases of production, there are
often several paths that the product goes
through before it is defined as scrap. Predictive
modeling can help identify the sequence of
paths that result in a high probability of a part
being scrapped, and bring this to light far ahead
in the production cycle, resulting in significant
savings.
•• Testing various mechanical parts during production is critical to ensure that the machines
built with them are of the best quality and have
optimal performance. For example, engines
built for airplanes must be tested rigorously
before they can be used. During production of
these engines, sensors mounted into them help
monitor their hundreds of components and
capture the interactions of various engine
parts. Predictive modeling can be employed to
identify patterns that would result in an engine
failure during the production phase itself, thus
allowing necessary changes to be made.
UTILITIES
Power company operators and distributors face
significant problems due to transmission losses,
meters that have been tampered with, and spikes
in demand. The distribution network is spread
across large regions, and the network, generators, transformers, turbines, and smart meters
are monitored for malfunctions. Any breakdown
could result in significant revenue loss for an
entire industrial sector or government. Similarly,
with the significant advances in instrumentation
technology, oil fields, rigs, and refineries have
now become more digital. Data generated by
thousands of sensors monitoring the functioning
of various parts of the oilfield is streamed in real
time to a centralized location. Predicting the possibility of a malfunction of any piece of equipment is critical so that corrective action can be
taken before a disaster occurs. Predictive modeling can be employed on data that is captured
in real time from the sensors connected to the
distribution network, for predictive maintenance.
In manufacturing, any machine downtime
can delay the production cycle and create a
significant bottleneck in the entire production
flow, resulting in heavy costs.
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Using Predictive Maintenance to Approach Zero Downtime
For example:
•• Predictive maintenance on assets such as gas
pipes and electrical cables used across the grid
can be an efficient and effective way to keep
costs down, risks low, and availability high.
•• Predictive modeling can be used to identify the
probability of smart meters being tampered
with and to plan preventive checks and maintenance of those meters.
•• Sensor data from smart grids can be used for
optimizing the grid and for identifying potential
failures in the distribution network and scheduling planned preventive maintenance to help
ensure zero downtime.
MEDICAL EQUIPMENT
The breakdown or malfunction of certain diagnostic and lifesaving or life-support equipment in
medical and pathology labs can be catastrophic.
Sensors within certain types of equipment are
used to record human body responses to diagnose critical illness so that appropriate care and
medication can be administered. It is therefore
critical to monitor this equipment in real time to
predict the probability of malfunction or breakdown and schedule preventive maintenance.
DATA CENTERS AND CLOUDS
Data volumes are growing rapidly, and organizations want to store all this data to mine information for any number of use cases in addition to
predictive maintenance. This data is business
critical and is typically stored on an elastic infrastructure in data centers. Many organizations are
also now moving to the cloud because it is easy
to make new services available to their clients,
who benefit from the pay-per-use model. It is
essential to have the data centers and cloud
infrastructure up and running all the time.
Unstructured data in the form of logs containing
the status of various services running on the
infrastructure, available in real time, can be
mined for predicting the failure of various services and performing preventive maintenance to
achieve zero downtime.
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Using Predictive Maintenance to Approach Zero Downtime
Enabling Predictive Maintenance
SAP® PREDICTIVE ANALYTICS
SAP® Predictive Analytics software has capabilities that make it a product of choice for building
robust models for predicting rare events in the
use cases just described.
Predictive maintenance data is time series data
derived from the readings from thousands of
sensors streamed at high velocity at real time.
Sensor readings reflect the state of the machine
performance at that time. So the time series data
must be transformed into a time-stamped population, and new derived features must be defined
to find out what changes in the sensor readings
over a period of time provide maximum information about the probability of failure of a machine.
One of the several ways of modeling data is
shown in Figure 1. Various statistical signatures
of each sensor are captured for a specific lookback period and associated with a segment of
data from the end of that period, to capture information of an event (such as machine failure)
during that segment to use in building predictive
models. Several such patterns can be generated
at a step size equal to the forward segment size.
The forward segment size defines the duration of
the validity of the forward prediction – that is, if
the segment size is 10 minutes, then the model
built will be able to give the probability of an
event occurring in the next 10 minutes.
The data manager component in SAP Predictive
Analytics enables the user to create ultrawide
analytical data sets for a time-stamped population, and automatically extract thousands of
derived features that can be used for creating
robust models.
If the event describes whether the machine has
failed or not, then the automated classification
component in SAP Predictive Analytics can be
used to identify the chances of machine failure
based on the ultrawide data set generated by the
data manager. On the other hand, if the event is
recorded as a continuous number, then the automated regression component in SAP Predictive
Analytics can be used to identify the relationship
of the sensor with the target. The classification
and regression algorithms can identify the key
influencers that have the most information to
explain the behavior of the event that is being
modeled. They also identify the positive, negative,
and neutral impact of the range of values of each
sensor involved in the event, enabling the user to
have an in-depth understanding of the causes
leading to the modeled event.
With SAP Predictive Analytics, the vast majority of
predictive maintenance use cases can be handled
automatically by a person with very little data
science expertise.
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Using Predictive Maintenance to Approach Zero Downtime
Figure 1: Data Preparation for Streams of Sensor Data
Raw sensor data stream
Time stamp
Machine ID
Sensor ID
18/01/2009 10:32:46
1020010
Sensor 1
0.924387
19/01/2009 10:32:46
542
Sensor 4
1.2235
Sensor 3
20/01/2009 10:32:46
1020010
21/01/2009 10:32:46
542
1
Sensor value
Processed sensor data
Machine ID: 1020010
3.397467
Time stamp
Sensor 1
Sensor 2
Sensor 3
123.876
18/01/2009 10:32:46
0.924387
3.600801
3.3997467
3.569223
3.950853
22/01/2009 10:32:46
1020010
Sensor ”n”
3.569223
18/01/2009 10:32:47
0.280205
3.920714
1.524253
23/01/2009 10:32:46
6482
Sensor 99
12887
18/01/2009 10:32:48
0.466136
1.23772
1.599199
24/01/2009 10:32:46
1020010
Sensor 2
3.600801
...
...
...
...
...
18/01/2009 10:32:50
0.44493
3.652581
25/01/2009 10:32:46 . . .
...
...
Sensor “n”
2.662753
...
3.137181
...
0.80788
Billions of rows – low data density
2
Data transformation
Look-back period
Step size
Normal
event
Time series
data stream
3
Abnormal
event
Ultrawide data sets
Millions of rows –
thousands of columns –
high information density
Time stamp
Machine ID
Type
Num. Sensors
Max_Sensor 1
...
...
Min_Sensor 1
20/01/2009 10:30
20/01/2009 11:00
...
...
...
...
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Using Predictive Maintenance to Approach Zero Downtime
The automated time series in conjunction with an
automated regression algorithm helps in problems related to forecasting, such as the remaining time before the machine or its parts break
down – in other words, the time to next maintenance. This is shown in Figure 2.
The automated clustering component enables
the user to find robust numbers of clusters in the
data and can be used for problems like grouping
machines behaving in a similar fashion and subsequent anomalies in the sensor data.
Figure 2: Time to Next Maintenance
On/off
1
0
Time
Accumulated operating hours
Next service
Now
Predicted
date
0
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Using Predictive Maintenance to Approach Zero Downtime
All the automated components build several
models internally and choose the most robust
models based on their predictive power and
predictive confidence. Predictive confidence corresponds to the proportion of information contained in the target variable that the explanatory
variables are able to explain – in other words, the
ability of the model to explain the target. Predictive power is the capacity of the model to achieve
the same performance when it is applied to a
new data set exhibiting the same characteristics
as the training data set. This is the generalization
ability or robustness of the model.
The models can be easily integrated into business processes and can be scheduled for automatic retraining if there is significant deviation in
the data or in the model’s performance. The vast
majority of predictive maintenance use cases can
be handled with an automated approach, and the
software can be easily used by a person with very
little or no data science expertise. For the most
complex problems, the expert analytics option in
SAP Predictive Analytics provides a workbench
where the automated algorithms can be combined with other prebuilt algorithms and with
custom algorithms written using the R programming language.
LEARN MORE
SAP® Predictive Analytics software is a powerful solution
designed to address several predictive modeling use cases
involving high-velocity data streams that capture machine
behavior in real time. To find out more about how this software can help your company use predictive maintenance to
achieve zero downtime, please contact your SAP representative or visit www.sap.com/predictive.
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