pahs - white paper

PAHS : Dec 2015 Version No : 1.0 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.

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