Predictive maintenance breakdown. Stay up and running. Control costs.
Predictive maintenance is the asset management practice of repairing an asset or piece of equipment before it fails based on data received about it. It is the third phase in asset management:
Corrective maintenance: repairs made after a problem or failure occurs
Preventative maintenance: scheduled repairs made based on experience
Predictive maintenance: repairs made because data for an asset indicates that a failure is imminent
What is an asset? IBM® looks to the International Organization of Standardization (ISO) 55000. An asset is a “thing, item or entity that has actual or potential value.” They are part of an organization’s physical infrastructure and include vehicles, electronics, fixtures, machinery, computers and more.
Predictive maintenance has emerged as a systems and software capability because more information has become available from assets themselves, and operational and maintenance functions have become digitized. Specific factors include:
- Availability of large amounts of data gathered through instrumented and connected assets
- Availability of data gathered through the Internet of Things (IoT)
- Convergence of IT with operational technology
- Advances in analytics to gain insights from data
- Artificial intelligence (AI) technologies such as machine learning — the ability for a system to learn from data on its own without programming
From fixing to predicting problems
If you can predict a problem before it occurs, is it still a problem?
Why is predictive maintenance important?
Fixing something before it breaks is more efficient and cost-effective than fixing it after it breaks. It helps…
- Avoid downtime and improve productivity
- Extend the life of assets and defer new purchases
- Reduce the cost and complexity of repairs
- Mitigate additional or related damage
- Meet regulatory standards and compliance
- Manage spare parts, materials and inventory
- And ultimately, boost the bottom line
These benefits are driving organizations to take advantage of predictive maintenance technologies and practices. According to IBM (PDF, 798 KB): “Across almost every asset-intensive industry (such as oil and gas, manufacturing or transportation), organizations are challenged with how to maximize the value of assets throughout their lifecycle.”
For example, an IBM Study highlights (PDF, 255 KB):
British designer and manufacturer of intelligent lighting and intelligent building solutions, PhotonStar Technology, develops systems that collect facilities and equipment metrics such as energy use and building occupancy, encrypts the information and consolidates it for analysis on the cloud. There, its customers use dashboards to track efficiency, create predictive maintenance plans and remotely monitor real-time status.
A Japanese automobile manufacturer uses IoT to model the behavior of their welding process. It wanted to identify causal factors of failures and faults and find top predictors of equipment failure. The system delivers 90 percent prediction of faults with no false positives; 50 percent of the faults are predicted over 2 hours in advance. The company saved 1.5 hours per fault thanks to advanced prediction.
A major aircraft manufacturer is using IoT to maintain calibration of precision assembly tools and improve manufacturing quality. Data from shop floor tools along with equipment failure data is used in predictive quality analytics to generate models that identify tools likely to need servicing. Faulty tools are proactively removed from the shop floor to be maintained and recalibrated, leading to significant improvements in manufacturing quality. The solution has enabled a 100 percent payback within one year — avoiding millions of dollars of rework and months of production delays by preventing out-of-alignment tools from remaining in the aircraft production workflow.
Key capabilities of effective predictive maintenance
Effective predictive maintenance harnesses the convergence of data from instrumentation and IoT with advanced analytics and AI technologies through digitized systems. IBM points to an A. T. Kearney survey in Industry Week (PDF, 798 KB) where 558 companies that used computerized maintenance management systems exhibited an average of:
- 28.3 percent increase in the productivity of maintenance
- 20.1 percent reduction in equipment downtime
- 19.4 percent savings in the cost of materials
- 17.8 percent decrease in inventory maintenance and repair
- 14.5 months payback time
To use these systems successfully, organizations need to:
As part of asset management, organizations must track, assess and manage the reliability of a wide range of physical and technological assets. Adding to this challenge is technology infrastructures running applications and data in silos. Integrating “siloed” systems improves visibility and efficiency in locating and communicating about potential failures.
IoT data such as weather-related information, RFID-enabled data, traffic information and information from other devices and sources can augment and strengthen predictive maintenance. For example, weather can affect external equipment in farming or oil and gas production or highly sensitive instruments in fields like healthcare and bio-technology. IoT can also consolidate information from potentially millions of pieces of equipment. Elevator and escalator maker, KONE Corp. for example, remotely monitors and optimizes management of more than 1.1 million elevators and escalators in buildings worldwide.
Analyze quality data
The ability to gather and analyze data about assets allows an organization to move from corrective to predictive maintenance. Predictive analytics and AI technologies such as machine learning can be applied to volumes of operational data to give organizations a more detailed and accurate understanding of equipment performance.
The quality or integrity of the data being analyzed is important, too. According to IBM (PDF, 798 KB), “the health of asset data is an often-overlooked failure. Without completed fields, or validated data, analysis is not possible. Analysis of the health of data fields in critical areas such as asset registries, item inventory and work completion is essential to supporting reliable analytic reports.”
Focus on reliability and efficiency
Building on the strengths of predictive analytics, reliability engineers can create statistically valid models of equipment life based on operational data and other factors. These models enable them to focus on critical risks that affect operational reliability and availability.
This capability also enables the development of a maintenance strategy that can improve efficiency: analysis may indicate current equipment maintenance schedules and practices are ideal and no changes need to be made. Or, prescribe maintenance sooner to avoid failure. Or, postpone maintenance to avoid unnecessary cost and effort.
Predictive maintenance resources
Making sense of the asset management spectrum
Explore the full asset management spectrum to help make the right choice, including how to start small and grow.
Intelligent connections: Reinventing enterprises with intelligent IoT
See predictive maintenance and asset management in a broader context and discover how IoT is changing operations and processes.
Hana Financial Group
Hana Financial Group worked directly with IBM Services in Korea to consolidate the infrastructure and resources of 11 of its affiliates, allowing it to take a proactive approach to maintenance and identify potential issues before they cause downtime.
VE Commercial Vehicles Ltd.
VECV simplifies and streamlines coverage for its multi-vendor environment across locations, accelerating issue resolution, increasing productivity, boosting availability and enhancing business continuity for its IT operations.
IBM Technology Support Services delivery difference
Reduce time and resource waste by up to 40% per year with predictive analytics and cognitive systems.