Asset Intelligence System A Practical Approach for Transforming from Reactive … · 2018-10-18 ·...

Asset Intelligence System: A Practical Approach for Transforming

from Reactive to Proactive Asset Performance Management

White Paper, MainTrain 2018 Conference

Dr. Ananth Seshan, Mr. Mrinal Chakravarty

5G Automatika Ltd.

203 Colonnade Road, Suite 202,

Ottawa, ON K2E 7K3.

+1-613-368-4809 | www.5gautomatika.com | [email protected]

July 2018

©2018. 5G Automatika Ltd. All Rights Reserved.

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Table of Contents

1. Introduction ................................................................................................................................................. 3

2. Strategic Importance of APM ....................................................................................................................... 4

3. Challenges .................................................................................................................................................... 4

4. Opportunity: Real-Time Asset Intelligence .................................................................................................. 6

5. Attributes of an Asset Intelligence System .................................................................................................. 6

6. Practical Opportunities to Improve Asset Performance using Asset Intelligence ....................................... 8

6.1 Opportunity 1: Removal of Non-Value-Added-Labour in Asset Usage Updates ....................................... 8

6.2 Opportunity 2: Asset Usage-Based Preventive Maintenance .................................................................... 9

6.3 Opportunity 3: Asset Condition-Based Predictive Maintenance ............................................................. 10

6.4 Opportunity 4: Trends or Rates of Change in Asset Performance ........................................................... 12

7. Conclusions ................................................................................................................................................ 13

8. References ................................................................................................................................................. 15


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1. Introduction

With the advent of internet, digital and AI-based learning techniques, the ability to effect proactive

Asset Performance Management (APM) has become a practical reality. Be it mining, oil and gas,

water or power utilities, or process and discrete manufacturing industries, major players are

embarking on using real-time intelligence to make informed proactive decisions using a universally

integrated IT infrastructure.

Such a transformation from a reactive regime of APM to a proactive one has its own set of

challenges. While e-maintenance, AI-based predictive techniques and decision support systems

have been in place for over a decade and a half [1-10], the practical implementations of such

systems are not yet widespread due to various reasons (further outlined in section 3), including, the

need to leverage on existing legacy infrastructure in manufacturing organizations that do not

generally lend themselves to the acquisition of raw data in a standard and straightforward manner.

A practical “logical rules engine” based approach is presented in this paper to initiate the

transformation from a reactive to a proactive regime in APM in the presence of disparate systems

including, but not limited to, existing legacy infrastructure. Such an approach could subsequently

expand to more advanced and sophisticated implementation of AI-based deep learning algorithms.

We shall introduce the concept of the Asset Intelligence System that comprises the logical rules

engine as an intermediary orchestrator between plant floor and enterprise systems to facilitate

proactive APM.

In other words, this paper shall discuss the challenges and opportunities for making the

transformation using an Asset Intelligence System. We shall also showcase some best practices for

the practical implementation of such a system in the industry by means of real use cases.


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2. Strategic Importance of APM

The main objectives of a well thought out APM program have been to maximize the availability and

reliability of assets. When these two metrics are improved, the return on (production) assets is

maximized. Given the fact that a major investment of the manufacturing (or utility) industry is on

production assets, achieving a better return on assets increases shareholder value - and is therefore

a strategic goal for most large enterprises.

Today, given the advancement in the state-of-the-art, it is possible to automatically capture

machine (production asset) data, transform the raw machine data to meta-data, discover

actionable intelligence from the meta-data and generate proactive maintenance actions to

maintain continuous and superior asset performance. Therefore, “opportunistic” maintenance

interventions can be generated in response to the “emerging” real-time intelligence on the

condition of an asset in the plant floor. This capability is useful beyond just maintaining operational

performance. In many instances, superior asset performance can result in a competitive advantage,

and conversely, asset degradation can relate to regulatory non-compliance, a safety risk, and/or, a

quality failure leading to a major financial loss. Therefore, APM is now approached by the industry

with strategic interest.

3. Challenges

Even though leaders in the industry are taking advantage of the major advancements in the area of

digital technologies to achieve effectiveness in APM programs – for instance, by introducing

Predictive and/or Proactive Maintenance of their assets, manufacturing companies by and large are

still practicing a predominantly reactive culture. That is, most large organizations are still allowing

their critical assets to fail without being able to predict the conditions of failure in advance and

preventing them. These organizations at best perform Preventive Maintenance (PM) but it is based

on calendar time. In other words, shutdowns of lines are planned, say, every two weeks for PM as

an example, and this is done without any significant evidence on whether the said two weeks (in

this case) are a good or a bad estimate of the time between the PMs. Similarly, a significant cost is

incurred in Corrective Maintenance and Emergency Maintenance.


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The reasons for this situation can be summarized as follows:

(a) Predictive actions or measures for preventing failures will require real-time intelligence on

the assets’ performance, which most organizations do not have.

(b) Such intelligence must be derived from raw data streams – and therefore, an infrastructure

to convert the plethora of raw plant data available into contextual intelligence, in real-time,

is required which most organizations have not put in place.

(c) Vertical bi-directional integration between plant floor IT systems (Historian/MES) and

Enterprise Asset Management (EAM) systems that allow for generating immediate actions

to prevent failures (predictive maintenance work orders) and record root causes in the EAM

systems directly from the plant floor are required. Such an integration has either been only

partial, unidirectional and/or not yet established in most large organizations.

(d) Most organizations have legacy systems that need to be integrated for data acquisition. The

functionality, design, operation and performance of the components of the legacy system

need to be understood in order to design the types of changes that will be required for

building object wrappers to each component - in order to make them open standard.

(e) Cost justification for the above-mentioned is not straightforward, requires a cross

disciplinary effort and is not widely held knowledge. In most cases, the justification is made

with incomplete data by teams from a single discipline and so is not convincing to the

management to favourably look at investments in this area. In other words, the total cost of

downtime, its impact on profitability and the return on physical assets is not fully

understood in the industry in quantitative terms, even though, there is a qualitative

understanding of the impact.


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4. Opportunity: Real-Time Asset Intelligence

As stated earlier, in order to perform good and continuously effective APM, there is a need for

generating proactive real-time actions that are based on intelligence and not data. In today’s

manufacturing environment, according to some reports, about 15,000 terabytes of data can be

generated every day. This is a lot of data!

Most of the collected data, unless converted into meta-data and further into intelligence, is not

useful. Asset Intelligence Systems are a breed of software programs that enable the collection of

raw data from the plant assets, controllers and drives and convert them into actionable intelligence.

The conversion process will involve the selection of data to be converted, aggregation of data in

multiple dimensions and automatically deriving some useful conclusions about the “status” or

“condition” of assets. The output of an Asset Intelligence System is the automatic generation of

useful and timely maintenance actions in resident asset management (or CMMS) systems in order

to proactively respond to a certain emerging behavior of assets that could result in a failure.

5. Attributes of an Asset Intelligence System

The following attributes are required for an Asset Intelligence System:

(a) It must enable seamless vertical integration between the plant floor assets and the

Enterprise Asset Management (EAM) or other legacy systems in a standard open systems

manner (as stated above) based on reusable libraries and specific wrappers.

(b) It must be capable of acquiring real-time raw data directly from machines using open

standards by interfacing with machine controllers (DCS), Historians or MES systems.


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(c) It should enable the continuous and real-time derivation of meta-data from raw data, based

on “contextual” information about the data.

(d) It must have the capability to represent logical conditions as a set of rules that can be

triggered should the logical condition be found true in real-time (real-time actionable

intelligence).

(e) It must be capable of predicting future probabilities of failure of machines based on

symptoms using deep learning algorithms.

Figure 1 below depicts the scope and functionality of such an Asset Intelligence System. The critical

element in this system is the automated workflows that get automatically enabled between plant

floor and enterprise IT systems based on real-time intelligence derived from the Asset Intelligence

System that resides in-between the two aforementioned IT systems.

Examples of such automated workflows are provided in Section 6.

Figure 1: The Transformation of Data into Actionable Intelligence and Actions


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6. Practical Opportunities to Improve Asset Performance using Asset Intelligence

6.1 Opportunity 1: Removal of Non-Value-Added-Labour in Asset Usage Updates

Preventive Maintenance (PM) is normally performed by tracking “elapsed time” (time-based PMs),

run hours, and/or, the number of cycles (usage-based PMs). The problem with using “elapsed time”

or Time-Based PMs is that machines could be under-maintained or over-maintained. Usage-Based

PMs are therefore more preferred because they present an opportunity to conduct PM

interventions based on actual data on usage rather than elapsed time.

In order to perform usage-based PMs, the usage data has to be updated periodically (say, every

shift or day) in order to ensure that the resident EAM system is able to generate a PM work order

when the usage crosses a certain threshold. The recording of such data in the EAM system happens

in a counter called “meters”. Meters are instantiated for every asset whose usage data must be

updated.

Typically, meter updates are manually entered by a human operator once in formatted paper forms

inside the plant application, and subsequently entered (copied) manually using the data input

screens in the enterprise application. As shown in Figure 2 below, an Asset Intelligence System can

establish a seamless connectivity and this will totally eliminate manual intervention, which means

two major benefits:

• Removal of a Non-Value-Added Activity and Cost of such labor.

• Removal of Potential Errors due to Human Fallibility.


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Figure 2: Automated Workflow for Updating Usage of Assets (“Meter Updates”)

Typically, this opportunity results in immediate cost savings by eliminating one, if not two, full-time,

non-value-added labor activities.

6.2 Opportunity 2: Asset Usage-Based Preventive Maintenance

As opposed to updating meters in an asset management system that in turn will trigger workflows

for usage-based maintenance actions when thresholds for PM are crossed, it may be useful at times

to generate maintenance work order actions directly when usage for an asset has crossed a limit.

This is made possible by aggregating the run times of an asset continuously using the Asset

Intelligence System. When the values of the run times cross a threshold value, a PM work order can

be automatically generated in a resident EAM system by the Asset Intelligence System.

The example shown below in Figure 3 is an illustration of this opportunity in a wastewater utility. In

this case, the Asset Intelligence System connects multiple lift pumps in the utility to the EAM

system and aggregates the usage of the lift pumps individually. When any of the lift pumps cross

the given threshold of 5000 hours, a PM work order is automatically generated in the EAM system.


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Figure 3: Automated Generation of a PM Work Order Based on Pump Usage

This opportunity improves (a) effectiveness of PM; (b) enforces “adequate” asset maintenance as opposed to “over-maintenance” or “under maintenance” which are both costly; and (c) improves the efficiency of the PM workflow by eliminating human inputs or systems that are not essential.

6.3 Opportunity 3: Asset Condition-Based Predictive Maintenance

The advantage of having an Asset Intelligence System is that a logical evaluation of an asset

condition can be made in real-time based on its performance and proactively acted upon. In other

words, process variables such as vibration, temperature, pressure, etc., can be logically combined

to understand the behavior of the asset in a holistic manner. This logical assessment can be used to

generate appropriate maintenance actions to predictively prevent a failure or even degradation in

asset performance.

The example below in Figure 4 is an illustration of a press shop responding opportunistically and

proactively to an emerging failure condition using an Asset Intelligence System. This case shows two


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process variables being monitored – spindle vibration and temperature. The logical condition

illustrated herein is both variables crossing a threshold and staying above the threshold for more

than (say) 5 minutes – which would be a symptom for failure of the presses. The Asset Intelligence

System can monitor the values of the two variables as well as the duration of time they exceed the

threshold, and can therefore generate a Predictive Maintenance work order if the duration of both

variables exceeds 5 minutes (in this case).

Figure 4: Predictive Maintenance Generated by Asset Intelligence (example) -

Triggers created for a work order when the spindle temperature AND spindle vibration increase

above threshold for more than 5 minutes in any of the 100 CNC presses.

Another potential example of Predictive Maintenance helping the reduction of waste is shown in

Figure 5. In this case, the Asset Intelligence System monitors the reject counts of the parts

produced by the assets and if they cross 2% in any 2-hour period during production, the Asset

Intelligence System generates a Predictive Maintenance work order. This is an illustration of how a

plant can proactively respond to a “current” situation and rectify the situation if possible as

opposed to a postmortem analysis after the shift. Typically, say, if the reject counts from a machine

is less than 1% and it is suddenly found to be 2% in a 2-hour period (by the Asset Intelligence

System), obviously something is going wrong and the tools and/or assets in question needs to be

checked. The Asset Intelligence System can enable such timely actions to prevent waste.


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Figure 5: Generating a Predictive Maintenance Workflow when the Number of Reject Counts

in any 2-hour Production Cycle is more than 2%.

6.4 Opportunity 4: Trends or Rates of Change in Asset Performance

The trends or rates of change of a process variable denotes the ‘velocity” in which a variable is

changing – which is, in most instances, more important than the absolute value of the variable

itself. In the example below which is applied to a process industry, the Asset Intelligence System

monitors the rates of change of pump temperature or discharge pressure drop – and if any of these

two values are greater than 1.5 times their last 30-minute average, it generates a Predictive

Maintenance work order automatically, as illustrated in Figure 6.


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Figure 6: Generating a Predictive Maintenance Workflow

based on “Rates of Change” of Multiple Process Variables.

7. Conclusions

APM needs to be a proactive activity in order to prevent unplanned downtime. Until recently, there

were limitations and challenges for implementing proactive APM. With the advent of digital

technologies, it is now possible to deploy intermediary orchestrating systems, such as Asset

Intelligence Systems, to link between disparate machines and IT systems to effect early detection of

failures and prevent them. In other words, such systems can perform predictive and preventive

(proactive) maintenance using a logic-based rules engine, and in parallel, collect relevant data (and

experience) to “learn” (using deep learning algorithms) to make more futuristic predictions of asset

failures.


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Since the above-mentioned Asset Intelligence System will have the capability of generating

actionable intelligence and automated workflows, it will result in the following opportunities for the

customer:

(a) Significant reduction in unplanned downtimes;

(b) Significant reduction in failure rates (or improvement in reliability);

(c) Removal of non-value-added labor due to the integration of workflows and business

processes between plant and enterprise;

(d) Unified visibility of production and maintenance information – in fact, maintenance actions

are driven by the need for enhanced performance;

(e) Reduction in the maintenance spend year on year;

(f) Mitigation of regulatory risks; and

(g) Due to most of the above-mentioned, increased return on (production) assets.


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8. References

1. Laura Swanson, “Linking maintenance strategies to performance”, Department of Management,

Southern Illinois University Edwardsville, Edwardsville, IL 62026-1100, USA, Int. J. Production Economics

70 (2001), pp. 237-244.

2. D.N.P. Murthy, A. Atrens, J.A. Eccleston (School of Physical Sciences), “Strategic Maintenance

Management”, Journal of Quality in Maintenance Engineering, Volume: 8 Issue: 4, 2002.

3. Mari Cruz Garcia, Miguel A Sans-Bobi, Javierdel Pico, “SIMAP: Intelligent System for Predictive

Maintenance: Application to the health condition monitoring of a wind turbine gearbox”, Computers in

Industry, Volume 57, Issue 6, August 2006, pp. 552-568.

4. R.C.M. Yam, P.W. Tse, L. Li, P. Tu, “Intelligent Predictive Decision Support System for Condition Based

Maintenance”, The International Journal of Advanced Manufacturing Technology, Volume 17, Issue 5,

Feb 2001, pp. 383-291.

5. M. Hosek, J. Krishnaswamy, J. Prochazka. “Intelligent Condition Monitoring and Fault Diagnostic System

for Predictive Maintenance”, US Patent 7,882,394, 2011.

6. R.B. Faiz, E.A. Edirisinghe. “Decision making for Predictive Maintenance in Asset Information

Management”, Interdisciplinary Journal of Information, Volume 4, 2009.

7. G.A. Susto, A. Shirru, S. Pampuri, “Machine Learning for Predictive Maintenance – A multiple classifier

approach”, IEEE Transactions on Industrial Informatics, Volume 11, Issue 3, 2015.

8. Jay Lee, Jun Ni, Dragan Djurdjanovic, Hai Qui, Haito Liao, “Intelligent Prognostics Tool and e-

maintenance”, Volume 57, Issue 6, August 2006.


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9. K. Wang, “Intelligent Predictive Maintenance – Industry 4.0 Scenario”, WIT Transactions on Industrial

Sciences, 2016.

10. Cesar E. Bravo, Luigi Saputelli, Francklin Rivas, Anna G Perez, Michael Nickolaou, George Zangl, Neil D

Guzman, Shahab Dean Mohaghegh, Gustavo Nunez, “State of the Art of Artificial Intelligence and

Predictive Analytics in the E&P Industry”, Volume 19, Issue 4, 2014.

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