PdM – Resources

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Predictive Maintenance - Glossary and resources

A broad set of resources from technology, data focused, engineering and manufacturing organisations describing how to leverage new technologies and methods, such as Internet of Things (IoT), Analytics, and Data Science, to modernise asset maintenance and management across all industry sectors.

These resources bring together the domain knowledge of manufacturing and engineering with the technology capabilities needed to maximise the maintenance value chain.

Glossary of terms

Artificial Intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalise, or learn from past experience. Britannica

Computerised Maintenance Management System (CMMS), also known as Computerised Maintenance Management Information System (CMMIS), is a software package that maintains a computer database of information about an organisation’s maintenance operations.  This information is intended to help maintenance workers do their jobs more effectively (for example, determining which machines require maintenance and which storerooms contain the spare parts they need) and to help management make informed decisions (for example, calculating the cost of machine breakdown repair versus preventive maintenance for each machine, possibly leading to better allocation of resources). Wikipedia

Enterprise Resource Planning (ERP) is the integrated management of core business processes, often in real-time and mediated by software and technology.

ERP is usually referred to as a category of business management software — typically a suite of integrated applications—that an organisation can use to collect, store, manage, and interpret data from these many business activities. Wikipedia

The Internet of Things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware (such as sensors), these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled.

The definition of the Internet of things has evolved due to convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of Things. Wikipedia

Machine Learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Wikipedia

Manufacturing Execution Systems (MES) are computerised systems used in manufacturing, to track and document the transformation of raw materials to finished goods. MES provides information that helps manufacturing decision makers understand how current conditions on the plant floor can be optimised to improve production output. MES works in real time to enable the control of multiple elements of the production process (e.g. inputs, personnel, machines and support services). Wikipedia

Predictive Maintenance (PdM) is a popular application of predictive analytics that can help businesses in several industries achieve high asset utilisation and savings in operational costs.

PdM directly monitors the condition and performance of assets during normal operation to reduce the likelihood of failures. It attempts to keep costs low by reducing the frequency of maintenance tasks, mitigating unplanned breakdowns and eliminating unnecessary preventive maintenance.

Resources

Disclaimer: The links below are external to The Data Lab website and are provided for illustration purposes only.
Inclusion here does not represent an endorsement by The Data Lab.

Technology Platforms

Several of the major technology companies have Predictive Maintenance offerings

On Demand Event ~ On demand webinar from Microsoft and Hitachi Solutions describing how IoT and predictive Analytics can transform manufacturing operations for the Digital Age. Leading experts and analysts discuss the state of manufacturing today—and a path to the future 

White Paper ~ White paper describing how analysing data from sensors can create business value by implementing Predictive Maintenance. Includes a framework for getting started with a Predictive Maintenance programme.

Demo ~ A step through demo of the integrated remote asset portal Rockwell Automation have built to see real time asset health and performance information, monitor service windows and carry out preemptive maintenance on parts and equipment before an incident occurs.

PdM Playbook ~ Detailed playbook for Predictive Maintenance including business and analytical guidelines and best practices to successfully develop and deploy PdM solutions using the Microsoft Azure AI platform technology.

Examples ~ A range of Predictive Maintenance sample solutions built and deployed on Microsoft’s Azure Platform. Includes code samples, step-by-step guides, etc.

TDS Overview ~ Describe the overall processes, capabilities for a Data Science Team tackling a challenge such as Predictive Maintenance using the Microsoft Azure technology stack.

PdM Introduction ~ A video introducing IBM’s approach to Predictive Maintenance.

PdM Example ~ A video of how IBM Analytical Decision Management has been implemented for Predictive Maintenance.

PA Guide ~ A guide to using Machine Learning Predictive Analytics which is a key component of any Predictive Maintenance implementation.

IoT Summary ~ Introduction to the Internet of Things (IoT) and the value that can be gained from analysing the streams of data produced by connected devices.

Article ~ An article on how to enable Predictive Maintenance capabilities within a manufacturing facility

IoT and PdM ~ Driving IoT enabled Predictive Maintenance – a summary of how Cloudera’s data platform can be used with IoT to implement Predictive Maintenance.

 

Consultancies

Several global and local consultancies have implemented PdM across industry sectors

PdM 4.0 ~ “Predictive Maintenance 4.0 – Predict the unpredictable” – detailed report from PwC and Maininnovation based upon a survey of 280 European companies on their current use of, and future plans for predictive maintenance.

Tool Guide ~ Article describing the value proposition for PdM and outlining 6 tools and techniques that all successful predictive maintenance programs should have.

On Demand Event ~ On demand webinar from Microsoft and Hitachi Solutions describing how IoT and predictive Analytics can transform manufacturing operations for the Digital Age. Leading experts and analysts discuss the state of manufacturing today—and a path to the future.

Report ~ In-depth global report from 2015 mapping the value from IoT across industries and assessing exactly how IoT technology can create real economic value. Predictive Maintenance is one of the major use cases explored within the report.

Podcast / Transcript ~ This podcast looks at a small number of factories worldwide who have stepped into the future of manufacturing by combing automation, artificial intelligence, and the Internet of Things to achieve game-changing productivity gains. 

Article ~ This article identifies the top 10 pitfalls that can derail a digital transformation programme within an organisation. PdM is a significant digital transformation programme. 

Website ~ Scottish Manufacturing Advisory Service (SMAS) – Scottish Enterprise service working across the whole manufacturing supply chain in Scotland. They work intensively with businesses across all sectors and of all sizes in Scotland, from Shetland to the Borders.

 

Manufacturing / Engineering Specialists

Specialist organisations that have a wealth of expertise implementing PdM in their domains

Community Home ~ Community portal for predictive, preventative, and proactive maintenance and also reliability engineering. Resources include articles, blogs, webinars, and step-by-step training videos

PdM Explained ~ Detailed introduction to Predictive Maintenance and suggested steps for implementing PdM

PdM Benefits ~ An infographic highlighting the range of benefits implementing PdM can bring for maintenance managers and others

Academy Videos ~ Collection of video courses seeking to show how to implement predictive maintenance and reliability engineering for an organisation

PdM Toolbox ~ Mathworks are a leading specialist scientific and engineering software development house. MATLAB is one of their core products. Their website includes a wealth of articles, documents, and videos that explain the challenges and solutions (based on Mathworks products) for predictive maintenance and many other domains

PdM eBook ~ Short eBook summarising the steps recommended for implementing Predictive Maintenance

PdM Videos ~ A series of short videos describing in increasing detail the steps recommended for implementing PdM. Each page includes links to other related resources

Common PdM problems ~ White paper describing how to overcome four of the most common challenges of implementing PdM using Mathworks software

ML for PdM Webinar ~ Webinar detailing the Mathworks capabilities for big data, machine learning, and deep learning, and how these can be combined with model-driven approaches to create and deploy predictive maintenance algorithms to embedded devices and cloud analytics platforms

Blog Articles ~ Sensor-Works designs, manufactures and markets advanced wireless sensors and value-added solutions to improve the monitoring of industrial machinery. There are multiple articles outlining how wireless sensors and condition monitoring can be used to implement PdM across multiple industries

Use Cases ~ A blog article describing some common uses cases for Predictive Maintenance across industries

White Paper  ~ Global automation specialist Emerson with many successful PdM implementations across multiple sectors. The highlighted white paper describes best practices for implementing a PdM programme while avoid the 10 most common pitfalls

PdM Introduction ~ CSS Electronics produce IOT / Controller Area Network (CAN) Bus data loggers and accessories. They have written a practical introduction to Predictive Maintenance for vehicles and machinery including typical challenges and suggested steps to take to implement PdM in these areas

Article ~ ScienceSoft are a specialist software and consulting company. This is an article describing how to implement IoT-based predictive maintenance in many industries. Includes use cases

Portal – Web portal publishing the latest reliability and up-time maintenance news and information for industry

Article ~ The article describes common reasons why PdM programme fails

 

Data Specialists

Data Science and Analytics organisations who have implemented PdM 

PdM Intro ~ AI Multiple.com are a website and marketplace focusing on Data and AI capabilities across all industries. This is an article introducing Predictive Maintenance

PM vs PdM ~ An article detailing the differences between Preventive Maintenance (PM), Predictive Maintenance (PdM) and Condition Based Maintenance (CBM) understanding when each is appropriate

IoT / PdM Introduction ~ Silicon Valley Data Science (now part of Apple) are a data science consultancy. They have produced a couple of detailed articles outlining the data science elements of PdM through a worked example based up a data set released by NASA from sensors on a set of gas turning engines ~ PdM Models

 

Article ~ InfoQ are knowledge and innovation publisher for software development professionals. This article describes the benefits and challenges from implementing Predictive Maintenance

Article ~ Worked examples describing the Machine Learning (ML) techniques most appropriate for PdM

Article ~ Article outlining how to get started with using machine learning methods and techniques for predictive maintenance from Data Science and Engineering specialists BDR