Extending Maximo into the Internet of Things with Condition Based and Predictive Maintenance IMT – 1915 Don Barry

James Crosskey

Dan Bigos

Please Note:

1

• IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion.

• Information regarding potential future products is intended to outline our general product direction and it

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remains at our sole discretion. • Performance is based on measurements and projections using standard IBM benchmarks in a controlled

environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

Truth and Trends in Asset Management Availability and Reliability

Fundamental ‘Truths’ about Asset Management Today Determining Asset Component Operating Context and Failure Patterns

First signs of troubleSmooth operation

Time

Per

form

ance

Time to failure

Failed

Failing

Warning Time

A

B

C

D

E

F

Approximately 11% of components of a complex asset fail over time

89% fail randomly over time

Addressing the 89% random failures with a combination of Asset Priorities Data, Operations Data, Maintenance Data supported by Technology ( CBM, PM, IoT, etc.) is key to being a leader in this challenge.

3

Trends in leading Asset Management predictive capabilities….

Source: Gartner

DescriptiveGet in touch with reality, a single

source of the truth, visibility

PredictiveUnderstand the most likely future

scenario, and its business implications

PrescriptiveCollaborate for maximum business

value, informed by advanced analytics

CognitiveDeeply analytical computing systems

that learn & interact naturally with people

What happened?

What will happen?

What should we do about it?

How do we optimize a dynamic, Big Data environment?

Volume (data at rest)

Velocity (data in motion)

Variety (many forms of data)

Veracity (data in doubt)

Watson Analytics

Source IBM GBS Digital Operations PAO

leading companies are driving toward predictive asset insight and beyond 4

Enterprise Asset Management + Asset Performance Management

5

will move customers to advanced stages of asset maturity

Instrumented Interconnected Intelligent

• Ever increasing range of sensors • Volume, velocity, variety • Event driven information

• Agility and Mobility • Highly Connected Systems • Cross Collaboration

• From data to actionable intelligence • From reactive to proactive • Whole lifecycle system optimization

Asset Management Maturity

Absent Cognizant Evolving Proficient Advanced

Maintenance is an Expense Maintenance is an Investment Time and Effort

Cos

ts

Performance

Calendar based Usage based

Predictive

Risk based

Condition based

Condition Based Maintenance (CBM) IoT

Customers struggle to gain meaningful insights from their assets

What is going on with all the assets which are critical to my business?

If I have an issue with one of my assets, how can I get an early warning so I can optimize maintenance schedules?

Can I avoid problems before they occur?

If my asset is failing, should I repair, replace, or shift load to extend it’s life?

How can I minimize repairs that are not needed?

I need to monitor device behaviors to understand anything that isn’t working as expected in real-time.

I need to detect that something is wrong and schedule maintenance before failure.

I need to forecast problems or situations and initiate appropriate response(s) to avoid unplanned down time.

I need insights from devices in the field to schedule repair only when needed.

I need to consider all the economic outcomes and risks associated with my asset strategy.

What is Condition Based Maintenance?

8

Condition Based Maintenance (CBM) is a maintenance strategy that uses the actual condition of the asset to decide what maintenance needs to be done

CBM dictates that maintenance should only be performed when certain indicators show signs of decreasing performance or upcoming failure. • Checking a machine for these indicators may include non-invasive measurements,

visual inspection, performance data and scheduled tests. • Condition data can be gathered at certain intervals, or continuously (as is done

when a machine has internal sensors).

CBM can be applied to mission critical and non-mission critical assets.

Condition Based Maintenance

Why Consider Condition Based Maintenance? •CBM is performed while the asset is working, this lowers disruptions to normal operations • Reduces the cost and frequency of asset failures • Improves equipment reliability

•Minimizes unscheduled downtime due to catastrophic failure

• Optimizes time spent on maintenance • Reduces overtime costs by scheduling the activities • Decreases requirement for emergency spare parts

•Optimized maintenance intervals

• Improves worker safety • Reduces the chances of collateral damage to the system

9

Reduction in overall capital spending

Working with better and faster asset information means more effective maintenance, which can extend the working life.

Reduction in maintenance costs

By identifying issues sooner, larger problems are avoided and less time is required to repair .

Reduction in SLA Penalties

Faster repairs means less unplanned downtime, and reduced exposure to service level agreement penalties.

Decrease in Reserve Standing Inventory

By getting more predictive in their approach to maintenance, customers can reduce the amount of idle backup inventory.t car

Cost Savings And

Risk Reduction

Potential Value of CBM

10%

10%

10% - 20%

5%

10

Data is critical in assessing asset condition

11

• Determining asset condition, requires access to a variety of information…

– Real time sensor data from the equipment via individual sensors (add-on or embedded)

– Real time sensor data or alerts from SCADA systems – Historical trends of sensor data from historian or other data repository – Inspection data that might include measurements, observations, photos

gathered manually or from mobile devices – Maintenance history of the asset – Related information such as environmental (temp, humidity, etc.), weather,

usage, output • For real time asset information, SCHAD can help integrate control system data with

Maximo for help with Condition Based Maintenance.

Assessing Asset

Condition

Connectivity to wide variety of operational data sources SCADA, OPC, PLCs, BMS to enable the Internet of Things

Connectivity of meter and sensor data from control systems to Maximo enables condition based maintenance for critical assets

Alarms defined and directed to field personnel, integrated with EAM activities in Maximo

IBM is partnering with SCHAD to integrate with control system data, to help our clients understand asset condition

Provide field personnel real-time access to asset performance and behavioral data from control systems

12

SCHAD Automatic Meter Reading

• Software product that provides connectivity to Control Systems (SCADA, PLC, BMS) via a wide range of industry standard protocols such as OPC, BacNet, etc.

• Enables mapping of sensors to assets in Maximo

• Allows data from control systems to be sent to Maximo, for use with existing Maximo Condition Monitoring capability

• Includes MQTT Integration to enable: • using IBM MessageSight for efficient and secure

remote site connectivity • providing data to IoT Foundation

• Ability to define simple rules for notifications or for creating work

orders/service requests in Maximo

SCHAD

SCHAD AMR Benefits • Reduces need for physical inspections or meter readings • Rapid connection of SCADA information into Maximo • Data can be used for trend analysis to optimize preventive maintenance • AMR is highly scalable, capable to measuring millions of data points across multiple sites into Maximo

13

Sensors provide information about the device

Introducing IoT Real-Time Insights

Maximo 1

2

Data comes in through IoT Foundation, IBM’s IoT cloud platform

5

Recommendations drive response in Maximo

Device

IoT Foundation

Data drives real-time analytics and business rules

IoT Real-Time

Insights

Data may be collected by a gateway device for connectivity & protocol translation

Rules trigger an action, such as creating a work order in Maximo

4

SCADA, historians

3 Real-time data

3a Data is enriched with master data from Maximo

Data, Alerts

Real-time dashboard

• Contextualizes device data • Monitors streaming data to detect situations • Acts on insights from the data

SCHAD

SCHAD is providing connectivity to SCADA, PLCs, BMS via OPC, BacNet, etc.

14

Predictive Maintenance Analytics

IBM Predictive Maintenance on Cloud

ingest

analyze report & recommend

profile

SaaS offering Prebuilt analytics Faster implementation & time to value Designed for line of business Reduces need for data scientists Insight at point of engagement

act

16

Ingest and obtain value from operational data

17

sour

ces

type

s • maintenance logs • inspection reports • repair invoices • operator profiles • test results

chan

nels

asset process product environment operations

Easily and quickly obtain insight

18

Load Content Pack Load Data

Train Model &Test Results

Predict

Obtain detailed insight into asset performance

19

CA 735A-02

Critical Asset 735A-02

(7.1.15-8.1.15)

735A-02

Predict asset performance

20

Critical Asset

Real-time monitoring & reporting

21

High

Low Low

Low Med 62

Maximo integration

22

Master data

Maintenance data (work orders) (scheduled PM, actual PM and BRK)

batch pull from Maximo real-time data push

batch pull from Maximo real-time data push

Initiate a work order or update an existing work order with maintenance recommendations

react prevent monitor predict

identification

criticality non-critical critical vital vital

complexity low medium high high

justification inertia cost effective when appropriate +ROI +ROI

maintenance tactic run to fail time / usage based, preventive maintenance inspect/sense, respond sense/predictive analysis,

respond

methods visual, aural inspection, time, usage sensors, connectivity, EAM, thresholds, rules

sensors, connectivity, EAM, maintenance & operational

history, multi-variate analytics

data none calendar, clock, meter real-time operational, environmental

historical & real-time operational, environmental,

maintenance

mttr

mtbf

scheduling backlog backlog prioritized automated

spares excess, expedite excess planned, reduced optimum

Where do CBM and PM fit in your maintenance strategy?

23

Trends in leading Asset Management predictive capabilities….

Source: Gartner

DescriptiveGet in touch with reality, a single

source of the truth, visibility

PredictiveUnderstand the most likely future

scenario, and its business implications

PrescriptiveCollaborate for maximum business

value, informed by advanced analytics

CognitiveDeeply analytical computing systems

that learn & interact naturally with people

What happened?

What will happen?

What should we do about it?

How do we optimize a dynamic, Big Data environment?

Volume (data at rest)

Velocity (data in motion)

Variety (many forms of data)

Veracity (data in doubt)

Watson Analytics

Source IBM GBS Digital Operations PAO

leading companies are driving toward predictive asset insight and beyond 24

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