PAHS - White Paper

PAHS : Dec 2015 Version No : 1.0 Page 1 of 13
Classification: Company Internal
PROGNOSTICS ASSET HEALTH SOLUTION - PAHS
WHITE PAPER for Water Treatment Plants Operating World Wide

AVS,India Whitepaper
List of Tables............................................................................................................. 3
List of Figures ........................................................................................................... 3
1. ABSTRACT.......................................................................................................... 4
2. INTODUCTION ................................................................................................... 5
3. PROBLEMS......................................................................................................... 6
4. IDEA – Prognostics Health Solution ..................................................................8
5. DETAIL – Prognostics health Solution............................................................... 9
1.1 Data Acquisition................................................................................................................. 9
1.2 Signal Pre-processing ........................................................................................................ 9
1.3 Data Cleaning ..................................................................................................................... 9
1.4 Alarm & Notification Management Unit .......................................................................... 9
1.5 Feature Extraction Method............................................................................................... 9
1.6 Diagnosis Method.............................................................................................................. 9
1.7 Prognostics Methods........................................................................................................10
6. EDS Asset Health Solution Capability............................................................... 12
7. CONCLUSION– Prognostics Health Solution....................................................13

LIST OF TABLES
Table 1: Major Problem Faced By Industries ........................................................................................................7
Table 2: User Centric Views on Prognostic Goal ................................................................................................. 11
LIST OF FIGURES
Figure 1: Asset Performance and Process Integration.........................................................................................5
Figure 2: Overview of prognostic approach ........................................................................................................6
Figure 3: PHM Overview.......................................................................................................................................8
Figure 4: High Level Prognostics..........................................................................................................................8
Figure 5: High Level Diagnostics ......................................................................................................................... 10
Figure 6: Goals for Prognostics............................................................................................................................ 11
Figure 7: High Level PAHS Implementation Approach.........................................................................................12
Figure 8: Predictive Maintenance Values .............................................................................................................13

1. ABSTRACT
AVS (Product & Engineering Solutions along with other Horizontals) has developed unique
expertise on the view of equipment life is moving from a deterministic view to a probabilistic
view of failures and remaining useful life (RUL), what does this mean for Asset Performance
Management?
What comes to mind when you envision the future of equipment reliability?
Where do these ideas come from?
Maintenance and reliability has traditionally held a backward-looking view of equipment life
driven by the well-known IPF model. Preventive maintenance and prediction technologies are
deterministic in nature and only detect potential failures after equipment damage has
occurred, and the RUL of the equipment is not well quantified.
To ensure the production/output and customer satisfaction in mining/ Heavy Industries
/Medical/Insurance/automobile sector the estimation of Remaining Useful Life of machineries
is a prime.
In this paper, we will walks you through trends in technology, weak signal analysis and the
application of big data to develop a vision of future possibilities for APM.

2. INTODUCTION
There are certain problems that almost every industry is facing now a days.Like with the
introduction of even more stringent emission limits engine developers are striving.
Long-term safe and economic operation, i.e., beyond 60 years, of the current fleet of nuclear
power plants (NPPs) of the United States. One of the main challenges in keeping these plants
operational is ensuring the integrity and performance of systems, structures, and
components (SSCs).
Only recently has industry begun to realise how ineffectively maintenance activities are
performed. Survey states that up to half of the maintenance performed is ineffective. This is a
major concern, because maintenance is directly related to asset reliability and availability. For
this reason, industry is experiencing an increased interest in physical asset management
(PAM).
Figure 1: Asset Performance and Process Integration
How can I
perform in
depth root cause
failure analysis
on my process
and equipment?
How can do I
create Highest
Quality
products?
Howe can I
reduce
process
variability?
How can I
ensure supply
is aligned with
demand?
How do I
achieve
optimal
equipment
efficiency and
availability?
How can I
predict an
impending
equipment
failure and the
cause?
What is the
life expectancy
of an asset's
component or
part?
How can I
optimize my
maintenance
plan?

3. PROBLEMS
Many a solution has been already developed and some are implemented in some or other
industry. But many of the necessary modules are still not included in their algorithm and thus
creating a gap for the end users to except the integrity of this solution with their existing
products.
Some of the module examples are:-
1. Don’t have open architecture.
2.Heavy customization is required to suit end user requirement.
3.Mostly they don’t have Open architecture and scalability to be reviewed.
4.Scalability might be an issue.
5.Prognostic capabilities are not incorporated or weak.
Figure 2: Overview of prognostic approach
Gaps in industries:-
The review of existing cost/benefit approaches revealed five gaps, as briefly summarised
below:-
1. Approaches developed by engineers for engineers.
2.Aerospace dominance
3.Approaches do not address overall system capability
4.Limited view about the benefits of PHM
5.Lack of direction for PHM
“It is tough to make prediction especially about future”:- Yogi Berra
What is
happening?
What will
happen?
What should
we do?
If we know what is likely to happen, we can prevent it, or plan it, and or plan to
reduce it impact.

Table 1: Major Problem Faced By Industries
Minimum return on assets
and decreasing
production volumes;
decreasing revenue,
decrease margins and
profitability
Optimize constrained
supply chain assets;
decrease volume
shipped
Decreasing asset
availability and increasing
labour and component
costs; minimize return on
assets.

4. IDEA – PROGNOSTICS HEALTH SOLUTION
Idea of PHM is:-
PHM =
Figure 3: PHM Overview
Remaining Useful Life (RUL) – The amount of time a component can be expected to continue
operating within its given specifications.
Figure 4: High Level Prognostics
We have use data-driven empirical approach that leverages your existing instrumentation and
IT infrastructures. Our solution will constantly samples data from your historian and analyses
the data to detect, diagnose, and prioritize impending problems.
The approach requires a diagnostic sensor to “sense” data that is above a pre-defined “good-
as-new” floor and below a “failed” ceiling.
DETECT DIAGNOSE PREDICT
Remaining
Useful life
WARNING! Normal
Operation
P1 P2

5. DETAIL – PROGNOSTICS HEALTH SOLUTION
1.1 DATA ACQUISITION
Data acquisition systems (abbreviated with the acronym DAS or DAQ) typically convert
analogue waveforms into digital values for processing.
1.2 SIGNAL PRE-PROCESSING
Pre-processing of data or data preparation includes cleaning and analyzing. Signal
processing of data is performed using following steps:-
1.3 DATA CLEANING
“Data cleaning is the number one problem in data warehousing”. Data cleaning
include:-
“Missing data correction, Noisy data removal, Outliers Removal.”
1.4 ALARM & NOTIFICATION MANAGEMENT UNIT
Includes Feature extraction from valid data points. Feature extraction transforms raw
signals into more informative signatures or fingerprints of a system.
1.5 FEATURE EXTRACTION METHOD
When the input data to an algorithm is too large to be processed and it is suspected to
be notoriously then the input data will be transformed into a reduced representation
set of features (also named features vector).Transforming the input data into the
set of features is called feature extraction.
1.6 DIAGNOSIS METHOD
A “fault” is another word for a problem. A “root cause” fault is a fundamental,
underlying problem that may lead to other problems and observable symptoms. A
root cause is also generally associated with procedures for repair.
Fault detection is recognizing that a problem has occurred, even if you don't yet know
the root cause. Faults may be detected by a variety of quantitative or qualitative
means. Automated fault detection and diagnosis depends heavily on input from
sensors or derived measures of performance.
DIAGNOSTICS METHODS/PHASES
• Off Line- Background Studies and Fault Mode Analysis.
• On Line- Perform real-time Fault Monitoring & Diagnosis.

Figure 5: High Level Diagnostics
1.7 PROGNOSTICS METHODS
• In this "meaning”, prognostic is called the prediction of a system’s lifetime and
corresponds to the last level of the classification of damage detection methods.
• Prognostics evaluates the current health of a component and, conditional on
future load and environmental exposure, estimates at what time the component
(or subsystem) will no longer operate within its stated specifications.
• Prognostic can also be defined as a probability measure: “A way to quantify the
chance that a machine operates without a fault or failure up to some future time”.
Prognostic could be split into 2 sub-activities:
• A first one to predict the evolution of a situation at a given time,
• A second one to assess this predicted situation with regards to an evaluation
referential.

Figure 6: Goals for Prognostics
Table 2: User Centric Views on Prognostic Goal
Category End User Goals Metrics
Engineering
Designer
Implement the prognostic
system within the constraints of
user specifications. Improve
performance by modifying
design
Reliability based metrics to
evaluate a design and identify
performance bottlenecks and
computational metric to meet
resource constraints.
Researcher
Develop and implement robust
performance assessment
algorithms with desired
confidence level
Accuracy and precision based
metrics that employ uncertainty
management and output
probabilistic prediction in
presence of uncertain
conditions.
Operation
Plant Manager
Resource allocation and mission
planning based on available
prognostic information.
Accuracy and precision based
metrics that predict RUL.
Maintainer
Plan Maintenance in advance to
reduce equipment downtime
and maximize availability.
Accuracy and Precision based
metrics that compute RUL
estimates based on damage
accumulation models.

6. EDS ASSET HEALTH SOLUTION CAPABILITY
POC has been already developed for one of the mining major for asset health solution for mobility
equipment’s to determine the remaining useful life. This prognostic application gives competitive
advantage for advance maintenance planning avoiding catastrophic failure and better inventory
management.
This prognostic application can also be integrated with existing ERP systems.
The EDS has the multidisciplinary subject matter experts for customize development of the solutions
for various assets like Clarifier, Water treatment equipment’s, Pumps, Compressors, Blowers, Boiler,
Turbine, Generator, Conveyors etc.
Figure 7: High Level PAHS Implementation Approach
AVS
FOR WATER

7. CONCLUSION– PROGNOSTICS HEALTH SOLUTION
Figure 8: Predictive Maintenance Values
Top reason choosing prognostic asset health solution:-
Any Source:-
Gain Insights from structured and unstructured data.
Real Time:-
Enable real time interaction across your value chain.
Analysis:-
Unlock new insights with predictive, complex analysis.
Applications:-
Run next –generation applications.
Innovation:-
Ultimate platform for business innovation.
Simplicity:-
Fewer layers, simpler landscape, lower cost.
Open architecture:-
Open choice at every layer to work with any preferred partners.

PAHS - White Paper

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