Assessment of Predictive Maintenance Systems: Technology Market Penetration and

Road Mapping (Technical Insights)

D2B4-TIMarch 2011

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Table of Contents

Executive Summary Research Scope Research Methodology

Overview Key Findings

Technology Snapshot and Trends Technology Capability Technology Value Chain

Impact Assessment and AnalysisMarket Impact of Existing and Emerging TechnologiesMarket Impact of Technology/Drivers and Challenges

Diffusion of Innovations and Needs Assessment Technology Adoption Cycle Demand Side Analysis

Executive Summary Research Scope Research Methodology

Overview Key Findings

Technology Snapshot and Trends Technology Capability Technology Value Chain

Impact Assessment and AnalysisMarket Impact of Existing and Emerging TechnologiesMarket Impact of Technology/Drivers and Challenges

Diffusion of Innovations and Needs Assessment Technology Adoption Cycle Demand Side Analysis

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Table of Contents

Opportunity Evaluation and Roadmapping Emerging OpportunitiesPotential Application – Available MarketTechnology Roadmap Technology Management Strategies

Appendix

Key Contacts and Patents

About Frost & Sullivan

Opportunity Evaluation and Roadmapping Emerging OpportunitiesPotential Application – Available MarketTechnology Roadmap Technology Management Strategies

Appendix

Key Contacts and Patents

About Frost & Sullivan

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Key Findings

11The key technology trends in the PdM industry is characterized by development of intelligent sensors, research on improved analyzing algorithms, integration of data collectors with analyzing software., development of integrated hardware systems specific to PdM, and improved communication channels with wireless sensor networks.

The key technology trends in the PdM industry is characterized by development of intelligent sensors, research on improved analyzing algorithms, integration of data collectors with analyzing software., development of integrated hardware systems specific to PdM, and improved communication channels with wireless sensor networks.

44

Some of these challenges are being solved by the following emerging technologies: a) MEMS based sensors being used in wireless sensor networks e.g. WSN products by MEMSIC, USA b) Improved algorithms based on Fast Fourier Transform and Pattern recognition e.g. products by Ivara, USA and research by IMS Center, USA c) Integrated systems for lower complexity e.g. products from Mettler Toledo, USA, Mistras Group, USA, etc. d) PdM software compatible with smart phones and PDAs e.g. portable system from Prosig, UK, e) Web-based PdM systems with wireless communication capabilities e.g. products from iSagacity, USA.

Some of these challenges are being solved by the following emerging technologies: a) MEMS based sensors being used in wireless sensor networks e.g. WSN products by MEMSIC, USA b) Improved algorithms based on Fast Fourier Transform and Pattern recognition e.g. products by Ivara, USA and research by IMS Center, USA c) Integrated systems for lower complexity e.g. products from Mettler Toledo, USA, Mistras Group, USA, etc. d) PdM software compatible with smart phones and PDAs e.g. portable system from Prosig, UK, e) Web-based PdM systems with wireless communication capabilities e.g. products from iSagacity, USA.

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The main business accelerator for the PdM industry is the need for near zero downtime and demand for automated corrective measures for plant equipment. The industry requires low cost and easy to install PdM systems, that are portable. The increasing use of wireless monitoring systems, need for prediction of trends in equipment performance, streamlining and prioritizing data on equipment condition, and improved reliability management needs are driving technology in this domain.

The main business accelerator for the PdM industry is the need for near zero downtime and demand for automated corrective measures for plant equipment. The industry requires low cost and easy to install PdM systems, that are portable. The increasing use of wireless monitoring systems, need for prediction of trends in equipment performance, streamlining and prioritizing data on equipment condition, and improved reliability management needs are driving technology in this domain.

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The key challenges in the PdM industry include slower rate of advancements in core sensor technology used in PdM, and drawbacks of MEMS sensors used in PdM. Slower rate of adoption of PdM software is also a significant restraint. The industry faces some obstacles in the near future because of lack of diversification in monitoring parameters of PdM, and low short term Return on Investment (ROI) for PdM. Installation barriers of web based PdM systems may lead to slower adoption rate for advanced PdM systems.

The key challenges in the PdM industry include slower rate of advancements in core sensor technology used in PdM, and drawbacks of MEMS sensors used in PdM. Slower rate of adoption of PdM software is also a significant restraint. The industry faces some obstacles in the near future because of lack of diversification in monitoring parameters of PdM, and low short term Return on Investment (ROI) for PdM. Installation barriers of web based PdM systems may lead to slower adoption rate for advanced PdM systems.

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Key Findings (Contd...)

55Among the key developments in sensors for PdM are the tri-axial MEMS based accelerometers used in a PdM system developed by Analog Devices, Intelligent high-temperature sensors by IMI Sensors, and Smart compressed air sensors by IFM Efector.

Among the key developments in sensors for PdM are the tri-axial MEMS based accelerometers used in a PdM system developed by Analog Devices, Intelligent high-temperature sensors by IMI Sensors, and Smart compressed air sensors by IFM Efector.

99Predictive maintenance systems are used predominantly in the manufacturing and process industry. Other important application areas include oil and gas, aerospace and marine defense systems, and electric power generation.

Predictive maintenance systems are used predominantly in the manufacturing and process industry. Other important application areas include oil and gas, aerospace and marine defense systems, and electric power generation.

66Among the key innovations in PdM software are the ‘Dynamic Inspection Routes’ based algorithm from Ivara USA, advanced data collectors for ultrasonic monitoring by CTRL systems, USA, and RPM based sampling enabled analyzer from SPM Instruments, Sweden

Among the key innovations in PdM software are the ‘Dynamic Inspection Routes’ based algorithm from Ivara USA, advanced data collectors for ultrasonic monitoring by CTRL systems, USA, and RPM based sampling enabled analyzer from SPM Instruments, Sweden

88Collaboration and partnership strategies among PdM manufacturers will accelerate wider usage of web based PdM systems, thereby enabling more efficient maintenance management. R&D strategies that include researching on better algorithms and low cost smart sensor systems are required.

Collaboration and partnership strategies among PdM manufacturers will accelerate wider usage of web based PdM systems, thereby enabling more efficient maintenance management. R&D strategies that include researching on better algorithms and low cost smart sensor systems are required.

77Developments in PdM software is seen to be more rapid compared to developments in sensor technologies for PdM. PdM manufacturers are also increasingly producing integrated PMS solutions in the market. The key innovations in integrated systems are ‘Peakvue’ embedded technology based systems from Coservices, USA, and Multi array electrochemical sensor based PdM systems by Aginova, USA.

Developments in PdM software is seen to be more rapid compared to developments in sensor technologies for PdM. PdM manufacturers are also increasingly producing integrated PMS solutions in the market. The key innovations in integrated systems are ‘Peakvue’ embedded technology based systems from Coservices, USA, and Multi array electrochemical sensor based PdM systems by Aginova, USA.

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Technology Value Chain

Predictive Maintenance Systems (PdM)

Vibration sensors Pressure sensors Temperature sensors Position sensors Key players: Analog Devices, Honeywell, BEI sensors, etc.

Vibration sensors Pressure sensors Temperature sensors Position sensors Key players: Analog Devices, Honeywell, BEI sensors, etc.

End Users

PdM Management PdM Management

Downtime Minimization Reduction in maintenance costs Preventing catastrophic equipment failure Reduction in maintenance staff Energy conservation Key players: DLI, SKF, SPM, Mistras, Mettler Toledo, etc.

Sectors: Manufacturing and process Oil and gas Aerospace and Defence Nuclear plants Marine equipments Wind farms Key Players: Masonite, Novartis, Irish Cement, Pfizer, GE, ELI Engineering, Pepsi, Marathon petroleum, etc.

Source: Frost & SullivanSource: Frost & Sullivan

Data analysis softwareDiagnosis softwareCorrective measures and tools Key players: Ivara, SKF, SPM, iSagacity, Emaint, etc.

Data analysis softwareDiagnosis softwareCorrective measures and tools Key players: Ivara, SKF, SPM, iSagacity, Emaint, etc.

Local detecting devices Local detecting devices

Central PdM devices Central PdM devices

Data collection devices Data storage devices Communication channels Key players: CSI, Entek, SKF, Commtest, etc.

Data collection devices Data storage devices Communication channels Key players: CSI, Entek, SKF, Commtest, etc.

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Key Business Drivers

Automated corrective measures through software are constantly being included in PDM systems, resulting in the development of advanced PDM software.

Demands for automated corrective measures

Constant need for miniaturization is a factor for the increased use of advanced MEMS based sensors and wireless sensors in PDM. Integration of PdM systems with handheld devices is also being rapidly adopted by maintenance personnel.

Need for portability and miniaturization

Factors Description

Near zero downtime requirements for plant equipment

Most plant managers are opting for better PdM analysis software with newer algorithms owing to their capabilities of better analysis of the collected data that leads to a lesser downtime of the machines.

Low cost and easy to install PdM systems.

Integration of entire PDM systems provides easy installation solutions to many PdM end users. This demand has led to the development of smart sensors. Smart sensors include a sensor (vibration/pressure/temperature etc.), a probe, and a data collector, that can be connected to a computer or a PDA analyze the data using a software.

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Key Business Drivers (Contd…)

On-field data collectors are being improvised to be able to connect to the data analyzers over wireless network connections. A few data collectors are even integrated into one device with the analyzer that is controlled remotely. These advancements let the maintenance personnel collect data and analyze it from different locations, thus improving reliability management.

Improved reliability management

The parameters that are used to monitor the condition of equipment are pre-determined by intelligent data collectors that in turn feed the collected data to high-end software. The interconnectivity between the various components of PdM is thus enhanced by manufacturing integrated solutions featuring all three components of the PdM value chain.

Streamlining and prioritizing data on equipment condition

Factors Description

Wireless monitoring systems

Adaption of wireless sensors networks are increasing due to the growing demands for un-tethered communication channels and wide area PDM coverage requirements. Wireless sensor networks involve collecting data from a large number of nodes, especially nodes placed in inaccessible or hazardous areas. More data collection provides the data analyzers with more information for more accurate results.

Prediction of trends in equipment performance

Analysis software is increasingly being equipped with pattern recognition algorithms in order to facilitate repetitive processes that could fully automate detection of performance trends based on historical data collected by the system.

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Key Business Challenges

PDM software has seen significant advances of late, and compared to the rate of newer developments in PdM software, the rate of adoption of such software is slower among the end users.

Slower rate of adoption of PdM software

Optical sensors are still used for very specific industries such as oil and gas pipeline monitoring. The advantages of fiber optic sensors are yet to be applied to industrial predictive maintenance, although research is on.

Limited use of fiber optic sensors in PdM

Factors Description

Slower rate of advancements in core sensor technology used in PdM

Advances in sensor architecture used for PdM are not as prominent as was a decade ago. The current trend among manufacturers of PdM systems is to integrate existing sensors with data collectors and communication channels to form smart sensors. Improvements in sensitivity and performance for sensors are required for the PdM market to grow.

Drawbacks of MEMS sensors used in PdM

Most advances in sensors used in PdM are MEMS based, and although adaption of these sensors have been growing, there are still certain drawbacks of MEMS when used for PdM systems. When compared to traditional piezoelectric sensors, MEMS sensors are more suitable for custom PdM solutions rather than standard systems.

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Key Business Challenges (Contd…)

Online monitoring systems are being upgraded with new advances in the internet technologies. However, complex issues are encountered by plant owners when it comes to setting up web based PdM systems. Most plants lack the communication infrastructure necessary to install, run, and maintain web based systems.

Installation barriers of web based PdM

With the advent of improved technology (specially advanced software that use technically advanced systems for operation), maintenance professionals need to be trained. Cost of training staff is often a barrier for plants to switch to predictive maintenance

Dearth of trained professionals to use complex systems

Factors Description

Lack of diversification in measuring techniques of PdM

Vibration analysis is still the most widely used method of PDM. The use of other methods such as pressure analysis, temperature analysis, ultrasound and infrared techniques, etc. have seen fewer recent developments

Low short term Return on Investment (ROI) for PdM

While plant managers are accepting the benefits of PdM systems with respect to downtime reduction, equipment efficiency improvement, and a good ROI in the long run, the initial cost of installing a PdM system is still a barrier for most plant owners

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Technology Life Cycle of Predictive Maintenance Systems

Lifecycle Assessment of PMS

DeclineMaturityResearch & Development

Growth

Time

Mar

ket P

enet

ratio

n

Source: Frost & Sullivan

33

44

11

55

6 & 76 & 7

1 – Capacitance based sensor for PMS:

2 – MEMS based sensors for PMS:

3 - Piezoelectric sensor based PMS:

4 – Pattern recognition analysis for PMS:

5 – FFT based software for PMS:

6 – Self correcting software systems for PMS:

7 – Intelligent sensor based int-hardware for PMS :

8 – In-built self-assessment systems

9 – Wi-Fi enabled hardware systems for PMS:

10- MRAC algorithm based software for PMS:

22 9

9

HighlightsHighlights

1010

88

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Opportunities for Predictive Maintenance Systems

Probability of Success

Low High

Low

High

Impa

ct/M

arke

t Att

racti

on

Opportunity Analysis Matrix for Component Technologies used in PdM Systems (based on current R&D status)

Opportunity Analysis Matrix for Component Technologies used in PdM Systems (based on current R&D status)

Capacitance based sensor for PMS :Capacitance based sensor for PMS :

Source: Frost & SullivanSource: Frost & Sullivan

Piezoelectric sensors:Piezoelectric sensors:

FFT based software based PMS:FFT based software based PMS:

Self correcting and intelligent sensor based PMS:Self correcting and intelligent sensor based PMS:

HighlightsHighlights

MRAC algorithm based software for PMS :MRAC algorithm based software for PMS :

Wi-Fi enabled hardware systems for PMS :Wi-Fi enabled hardware systems for PMS :

Pattern recognition analysis:Pattern recognition analysis:

MEMS based sensors:MEMS based sensors:

Self correcting and intelligent sensor based PMS:Self correcting and intelligent sensor based PMS:

In-built self assessment systems:In-built self assessment systems:

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Potential Applications – Opportunity Analysis

Probability of Success

Level of

Att

racti

ven

ess

Source: Frost & Sullivan

Manufacturing

Large scale Research facility

Oil and Gas

Power Generation

Defence- marine and Aerospace

Food processing and pharmaceutical

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Technology Management Strategies

Analysis

Downtime Minimization Reduction in maintenance costs Preventing catastrophic equipment failure Reduction in maintenance staffEnergy conservation

Relevant Trends

Zero downtime Synchronization of data flow Streamlining and prioritizing data Reduction in installation costs Intelligent self-assessment

Unmet Needs

Downtime Minimization Reduction in maintenance costs Preventing catastrophic equipment failure Reduction in maintenance staffEnergy conservation

Relevant Trends

Web based PdM systems Integrated data collectors with analyzers Advanced analysis algorithms Advanced low cost sensors Smart sensor systems used for PdM

Relevant Trends

Collaboration Strategies: PdM solution provides could form collaborations with communication providers to enable plant owners install state-of-the-art network facilities in order to use web based PdM systems efficiently, thus further reducing downtime. R&D Strategies: Research and development can be broken down into sensor technology and software algorithms. The recommended strategies in these two sectors are:

Key players in the PdM market should invest on research of sensors that are more sensitive and cost efficient. The advantages of fiber optic sensors should be explored in a broader spectrum of predictive maintenance systems. A few consortiums like the Center for Intelligent Maintenance Systems (IMS) are presently researching on advanced algorithms that would help prioritize the data collected from sensors based on baseline conditions. PdM software manufacturers should participate in such research and development activities.

Partnership Strategies: Various PdM software developers and PdM sensor manufacturers should form partnerships with peers to achieve the need of fully automated data collection processes, and building intelligent equipment with built in self assessment capabilities

Strategic Recommendations Collaboration Strategies: PdM solution provides could form collaborations with communication providers to enable plant owners install state-of-the-art network facilities in order to use web based PdM systems efficiently, thus further reducing downtime. R&D Strategies: Research and development can be broken down into sensor technology and software algorithms. The recommended strategies in these two sectors are:

Key players in the PdM market should invest on research of sensors that are more sensitive and cost efficient. The advantages of fiber optic sensors should be explored in a broader spectrum of predictive maintenance systems. A few consortiums like the Center for Intelligent Maintenance Systems (IMS) are presently researching on advanced algorithms that would help prioritize the data collected from sensors based on baseline conditions. PdM software manufacturers should participate in such research and development activities.

Partnership Strategies: Various PdM software developers and PdM sensor manufacturers should form partnerships with peers to achieve the need of fully automated data collection processes, and building intelligent equipment with built in self assessment capabilities.

Strategic Recommendations

R&D Strategi

R&D Strategies: The recommended strategies in sensor technology and software algorithms are: Key players in the PdM market should invest on research of sensors that are more sensitive and cost efficient. The advantages of fiber optic sensors should be explored in a broader spectrum of predictive maintenance systems. Consortiums such as the Center for Intelligent Maintenance Systems (IMS) are presently researching advanced algorithms that would help prioritize the data collected from sensors based on baseline conditions. PdM software manufacturers should participate in such research and development activities.

Partnership Strategies: Various PdM software developers and PdM sensor manufacturers should form partnerships with peers to achieve the needed fully automated data collection processes, and building intelligent equipment with built in self assessment capabilities. Collaboration Strategies: PdM solution providers could form collaborations with communication providers to enable plant owners install state-of-the-art network facilities in order to use web based PdM systems efficiently, thereby reducing downtime further.

Strategic Recommendations