More than $100,000 – that’s the potential saving on the correctly predicted failure of a large asset in many industries, according to a study conducted in 2023. The market for predictive maintenance solutions alone is projected to grow by nearly 20% annually to 2028. While the solutions themselves help many organizations cut downtime costs and plan maintenance work, they still come with certain challenges that not every company can address.
- 1. What is predictive maintenance?
- 2. IoT maturity levels and their importance in predictive maintenance technologies
- 3. Benefits of predictive maintenance
- 4. Predictive maintenance vs preventive maintenance
- 5. Implementing predictive maintenance solutions
- 6. Predictive maintenance tools and technologies
- 7. Challenges of predictive maintenance
- 8. FAQ
What is predictive maintenance?
Predictive maintenance is a proactive approach to monitoring equipment to ensure the timely maintenance of hardware and prevent failures and downtime. Predictive maintenance relies on condition monitoring, analyses of historical data, sensor data, failure data, and standard data sets for failure prediction. Unlike reactive maintenance, where equipment is repaired after an error or failure occurs, steps can be taken before downtime strikes.
IoT maturity levels and their importance in predictive maintenance technologies
Not all companies that are thinking about predictive maintenance are at the same level of maturity. Owning devices, connecting to the cloud or even having IoT solutions or sensors does not yet mean that one has the right tools for autonomous decision-making.
Knowing your Internet of Things maturity level and how to head toward the next stage will make it easier for you to plan your digital transformation without any slip-ups. Below you will find a brief overview of the maturity levels and the various technologies needed at each stage.
- Digitization and connectivity as a foundation
- Centralized data and real-time visibility
- BI for data insight
- Machine Learning for future forecasting
- Loop closure to control and manage
- MLOps for automation and improvement
- Artificial Intelligence / Machine Learning for autonomous decisions
Benefits of predictive maintenance
Predictive maintenance solutions allow manufacturers to foresee equipment failures before they lead to a breakdown. But the benefits are much greater than that! This proactive approach to hardware maintenance also helps businesses save money on unnecessary tasks. You can also schedule maintenance activities easily in advance, thus prolonging the lifetime of each piece of equipment. How measurable are these benefits?
As per Deloitte’s report on PdM solutions, they lead to 15% less downtime, a 20% increase in labor productivity, and up to a 30% decrease in inventory levels due to a reduced need to keep assets and maintenance parts on-site.
Increasing reliability
Many businesses rely on machines, and their operability directly translates into financial results. Imagine a rocket engine manufacturer that can’t deliver parts for scheduled flight tests due to a production line malfunction. Or a pharmaceutical company that, due to an unforeseen failure, cannot provide a batch of drugs or vaccines on time, directly affecting the budget, health, and even human lives.
Using predictive maintenance solutions gives insight into the wear and tear of fleet components. This is important in critical industries that don’t have much time to react to errors. A proactive approach increases confidence that production will not be impacted.
Saving costs
According to the IoT Analytics portal, the median cost of unplanned downtime throughout over 10 industries is now about $125k per hour. By monitoring the condition of systems and equipment in real– time or based on historical data, a successfully implemented predictive maintenance program can prevent you from incurring such harsh downtime costs and save you time on unplanned repairs.
Prolonging the life of machines
The operation of any industrial machinery involves technical inspections. Sometimes seemingly well-functioning hardware can have unnoticeable signs of wear and tear. Today, data analysis of key parameters makes it possible to detect potential errors in advance. The continuous monitoring of parameters such as energy consumption, vibration analysis, and temperature is possible. The returns on investment are quickly apparent. The IoT Analytics report indicates that 95% of predictive maintenance adopters reported a positive ROI, with almost 30% communicating amortization in less than one year.
Reducing maintenance work and downtime
In 2022, each plant operated by a Fortune Global 500 company incurred an average annual cost of almost $130m due to unplanned downtime. Downtimes cannot be avoided completely; some work is needed for servicing equipment. It’s all about having control over it and skillfully planning WHEN it happens, not IF it happens. Implementing a predictive maintenance strategy can significantly reduce unpredicted downtimes. Instead of waiting for equipment to fail, predictive maintenance work involves monitoring equipment performance and predicting when such maintenance should be taken care of.
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Predictive maintenance vs preventive maintenance
Types of predictive maintenance
- Predicting indirect failure
Aim: Evaluating machine condition
Methods: Data Analysis, Supervised Machine Learning
Benefits: Better scalability and cost-effectiveness
Limitations: Depending on historical data and not real-time data ––for modeling to be accurate, huge sets of historical data need to be analyzed
- Detecting anomalies
Aim: Finding anomalies in data indicating machine errors or malfunctions
Methods: Analyzing the standard data set (not the failure data)
Benefits: You only need a small amount of computing capacity to train a model
Limitations: Failure time–window accuracy – lack of knowledge of when a failure may occur
- Remaining Useful Life (RUL)
Aim: Assessing the remaining operability of the machine before a fix or replacement
Methods: Analyzing data from sensors
Benefits: You get accurate information of the time window for failure
Limitations: Hardware requirements – high computing power needed to train models
- Condition-Based Maintenance (CBM)
Aim: This maintenance method is aimed at monitoring the actual condition of the asset
Method: Checking a machine for specific indicators including measurements, visual inspection, checking on performance data, and scheduled tests
Benefits: Performed while the asset is working, which minimizes the probability of operational disruption
Limitations: Expertise in data analysis and incurring the cost of equipment (condition sensors for CBM)
Implementing predictive maintenance solutions
Being aware of the benefits, companies are wondering where to start using predictive maintenance solutions. The strategy, also referred to as the predictive maintenance program, includes 8 important steps.
8 steps to implementing a predictive maintenance program
- Budget and planning – before you launch anything or acquire any sensors, you need a budget to implement the program, the support of the board of directors, and the assurance that you will get access to all the necessary data from other departments (e.g., on repair costs).
- Identify devices – select the machines you want to focus on. Not all assets will need to be part of a PdM implementation program. Additionally, by focusing on a selected area and operating on a smaller scale, you can take a pilot approach and simply adjust the action plan later based on results. For this purpose, analyze the criticality of your assets. Take into account which machines need a lot of investment, directly affect production lines, or which are extremely costly to repair.
- Collect data – you certainly have data from multiple systems. You need to select actionable data first. For this purpose, review the machine manufacturer (OEM) maintenance information (inspection times, equipment behavior)
,and collect historical in-house data (regarding maintenance and inspection data), but also costs incurred (as provided by accounting or procurement systems). - Analyze historical data on failures – do you know if there are any recurring patterns of failure? This is the time to have a closer look at it. How difficult was it to identify the source of failures in the past? What were the reasons behind them? How often did they happen and how did they affect machines and production (that is, what was their impact and severity)? The answers to all these questions will allow you to move on to implementing the right solutions.
- Implement CBM techniques – condition-based monitoring is an important aspect of implementing a PdM program. You will need to choose and implement sensors for measuring the parameters of your choice (thermometers, tachometers, endoscopes, thermal cameras, leak detectors, and accelerometers),
- Use predictive algorithms – in the previous steps you focused on historical data. Now it’s finally time to look into the future. To do this, data scientists will use the collected sensor data and create algorithmic models to predict the remaining useful life (RUL) and component wear and tear in the context of possible future failures (RTTF – remaining time to failure).
- Start a pilot program – it is now time to deploy the technology on selected assets and start a pilot program. Consider using sensors connected to the cloud and taking advantage of predictive maintenance software to send automated alerts on anomalies.
- Optimize – over time you will see if the program is a success. You will have new data on failures and costs saved. You may decide to expand the program or take corrective actions.
Predictive maintenance tools and technologies
Predictive maintenance tools and technologies have revolutionized the way companies maintain their equipment and machinery parks in many industries. Key features of predictive maintenance software include real-time monitoring of equipment, data analytics to identify patterns and trends, and the ability to generate alerts and notifications when maintenance is needed, plus a full range of additional tools for measuring individual parameters and technologies, such as a digital twin or Machine Learning for predictive analysis.
Challenges of predictive maintenance
Implementing a successful predictive maintenance program can be a complex and laborious task. Some of the key challenges include integrating predictive maintenance into existing maintenance management systems, investment in staff competencies, or choosing the right tools and systems (this will be discussed further in an upcoming article on predictive maintenance software).
Budget planning
IoT programs, including the implementation of predictive maintenance, require reasonable planning; hence, you are investing time and money in 3 crucial areas: software, hardware, and… people. Long projects of this type create challenges for budgeting since at each stage a different area is addressed and other skills required. It’s easy to make a mistake, so many companies turn to experienced vendors to help create a strategy and build optimal solutions.
Accuracy of predictive maintenance solutions
According to data from an IoT Analytics report, the accuracy of predictive maintenance solutions available is now near 50%. It is not ideal; however, they are constantly being developed and improved with the help of AI and ML modeling technologies. This means, first of all, that technology is still on the rise, and thus it is worth considering which very specific solutions you need.
A competent maintenance team
Not everyone in the organization has the competencies to perform maintenance activities based on advanced analytics. Implementing a PdM program will involve investing in the competencies of your staff, often involving experts who will conduct and oversee the pilot phase. It may also prove useful to delegate the maintenance of IoT solutions to an external company or to use an IoT platform that has all the capabilities, including security, out of the box.
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Summary
The importance of maintenance cannot be overstated. Scheduled maintenance is essential for keeping equipment running smoothly and avoiding unexpected downtimes. By adhering to a schedule maintenance plan, businesses can reduce the risk of equipment failure and costly repairs. However, even with regular maintenance, there is still the possibility of a breakdown. This is where predictive maintenance comes in. When implementing these solutions, it is important to be aware of the strategy, to plan skillfully, and to adapt the technological solutions to the organization’s IoT maturity level.
FAQ
What are the 4 types of maintenance strategies?
The major types of maintenance strategies include run-to-failure, preventive, predictive, and reliability-centered maintenance.
What is prescriptive maintenance?
Prescriptive maintenance is also referred to as RxM. It is a strategy that includes monitoring the condition of assets with specific maintenance recommendations.
What is predictive maintenance and what are some examples?
An example of predictive maintenance could be the use of a system that detects wear and tear on wind turbines by analyzing sensor data.
Does predictive analytics use AI?
Predictive analytics uses AI and ML to detect trends and anomalies in data that would be difficult for a human to catch – for example, in large amounts of data from various data sources.
- 1. What is predictive maintenance?
- 2. IoT maturity levels and their importance in predictive maintenance technologies
- 3. Benefits of predictive maintenance
- 4. Predictive maintenance vs preventive maintenance
- 5. Implementing predictive maintenance solutions
- 6. Predictive maintenance tools and technologies
- 7. Challenges of predictive maintenance
- 8. FAQ