In one of our recent articles, we already discussed what is Machine Learning, along with some of the most common implementations in Banking, Retail, Finance, and Healthcare. This time we will focus on predictive maintenance with Machine Learning and determine how effective it is in 2024.
This year, the importance of this technology for business continues to grow. According to a study by Deloitte, predictive maintenance decreases unexpected breakdowns by 70%, boosts operational productivity by 25%, and lowers maintenance costs by 25%. So, let’s discuss in great detail how to get the most out of this approach.
What Is Predictive Maintenance?
This is a proactive maintenance strategy that uses Data Analytics, Machine Learning, and other predictive techniques to forecast when equipment or machinery is likely to fail, allowing maintenance to be performed before an expensive breakdown occurs.
In predictive maintenance, historical data from sensors, IoT devices, and other sources is analyzed to identify patterns and trends that can lead to equipment failure. By monitoring factors such as temperature, vibration, pressure, and usage patterns, predictive maintenance algorithms can foresee potential issues and recommend maintenance actions to prevent downtime, reduce maintenance costs, and optimize asset performance.
How Does a Predictive Maintenance Model Work?
The process of predictive maintenance Machine Learning typically involves the following steps.
Real-Time Data Input
Real-time data from sensors, IoT devices, or other sources is continuously collected and fed into the predictive maintenance model. Data collected from devices includes various metrics related to equipment health, normal operating conditions, and environmental factors. Sensors embedded in equipment measure parameters such as temperature, vibration, pressure, fluid levels, and electrical currents. In addition to real-time data, historical maintenance records, failure patterns logs, and equipment performance data are also added to the model for more context.
Prediction Generation
At the second stage, the predictive maintenance model analyzes the incoming data to identify patterns, correlations, and anomalies indicative of potential equipment failures or maintenance needs. Machine Learning is used to deliver the core functionality, and is responsible for data processing and prediction generation. Based on the analysis of the input data and the model validation, the predictive maintenance model generates accurate predictions about the likelihood of equipment failures or maintenance requirements. These predictions may be expressed as probability scores, alerts, or recommended actions.
Alerts and Notifications
When the predictive maintenance model completes proactive identification of a potential failure or maintenance issue, it triggers alerts or notifications to relevant stakeholders. These alerts may be communicated through various channels such as email, SMS, or integrated directly into the organization’s monitoring systems. Alerts serve as an early warning system, allowing maintenance teams to take proactive measures to address potential issues before they escalate into costly failures.
Decision-Making and Action
Being aware of the predictive insights, maintenance teams can make informed decisions about scheduling maintenance activities. Maintenance tasks can be prioritized based on the severity and urgency of the predicted issues. Predictive maintenance helps optimize resource allocation by ensuring that maintenance efforts are directed toward the most critical areas.
Predictive vs Preventive vs Reactive Maintenance
To avoid any confusion between the three related concepts, let’s talk about the distinctions.
Predictive Maintenance (PdM)
Involves using Data Analytics, Machine Learning, and other predictive techniques to anticipate equipment failures and schedule maintenance activities accordingly.
Distinctive features:
- Predictive maintenance usually relies on real-time data collection from sensors, IoT devices, and other sources to monitor equipment health and performance.
- By predicting when equipment is likely to fail, it allows maintenance activities to be scheduled proactively.
- Maintenance tasks are performed based on the actual condition of the equipment, rather than on a fixed schedule or usage threshold.
Serhii Leleko
ML & AI Engineer at SPD Technology
“To provide an example of this, an algorithm can analyze vibration data from a rotating machine and predict an impending bearing failure based on abnormal vibration patterns. Maintenance will be scheduled to replace the bearing before it fails, preventing unplanned downtime.”
Preventive Maintenance (PM)
Regularly scheduled maintenance tasks are performed at predetermined intervals or based on usage thresholds, regardless of the equipment’s actual condition.
Distinctive features:
- Maintenance tasks are performed according to a predefined schedule, typically based on calendar intervals or usage thresholds, for example, a certain number of operating hours.
- Maintenance activities are performed regardless of whether there are signs of equipment degradation or failure.
- Preventive Maintenance tasks are planned and scheduled in advance, allowing for efficient allocation of resources and manpower.
Reactive Maintenance or Corrective Maintenance
Responding to equipment failures or breakdowns as they occur, without prior planning or scheduling.
Distinctive features:
- Maintenance activities are triggered in response to equipment failures or malfunctions, often resulting in unplanned downtime and production losses.
- The primary focus of Reactive Maintenance is on repairing machinery to restore functionality, rather than preventing failures from occurring in the first place and ensuring that equipment operates properly.
- Reactive Maintenance can be costly due to the expenses associated with emergency repairs, replacement parts, and lost productivity.
Types of Machine Learning Models Used in Predictive Maintenance
Machine Learning models play a crucial role in predictive maintenance and help to analyze existing data, identify patterns, and make predictions about failures or equipment maintenance needs. Here are the most common models for this use case.
Supervised Learning
It involves training a model on labeled data, where the input features (predictors) are associated with corresponding output labels (responses).
Supervised learning models include:
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- Neural Networks
- Logistic Regression
These classification models can be used for binary or multi-class tasks. For example, a classifier can predict whether a piece of equipment will fail within a certain time frame based on sensor data and historical failure pattern records.
Supervised learning is quite popular in other industries as well, like Predictive Analytics in Retail, where it is used for tasks such as demand forecasting, customer segmentation, and personalized recommendation systems. Retailers leverage supervised learning to predict customer purchasing behavior based on historical transaction data and demographics.
Unsupervised Learning
With this approach, a predictive model is trained on unlabeled data to discover hidden patterns, structures, or relationships within the data. Unsupervised learning models such as K-means clustering, Gaussian mixture models, and Autoencoders identify suspicious behavior, trends, or outliers in sensor data. This anomaly detection with Machine Learning may help to identify potential equipment failures or abnormal operating conditions that require further investigation.
Semi-Supervised Learning
Semi-supervised learning is a hybrid approach incorporating elements of both supervised and unsupervised learning. It typically works with a small amount of labeled data augmented by a larger pool of unlabeled data. This method can be especially beneficial in predictive maintenance, where active learning strategies are implemented to select the instances most informative for labeling, thereby maximizing the utility of available labeled data.
Likewise, Machine learning in the finance sector widely employs semi-supervised learning for various Machine Learning applications, such as credit scoring, fraud detection, and market anomaly detection. These tasks often involve recognizing unusual patterns in financial transactions, which could indicate fraudulent activities or market irregularities. The intriguing correlation between these two fields manifests the adaptability of semi-supervised learning and its capacity to handle different types of data in varied contexts.
Reinforcement Learning
It involves training an agent to interact with an environment to maximize cumulative rewards through a trial-and-error learning process. Reinforcement learning algorithms can be used to optimize maintenance process scheduling decisions in dynamic environments.
For example, an agent can learn to adaptively schedule maintenance tasks based on:
- Real-time equipment health monitoring data
- Operational constraints
- Cost considerations.
Using reinforcement learning for predictive maintenance has parallels with Machine learning healthcare applications, in terms of the strategy of learning from past instances to make future decisions. In predictive maintenance, reinforcement learning utilizes historical machine data and operational behavior to suggest optimal maintenance strategies. Similarly, in healthcare, it has been successfully applied in determining treatment plans based on patient characteristics and previous responses to interventions. This machine learning technique’s capability to recommend personalized courses of action based on past data demonstrates its cross-sector versatility and efficacy.
The Benefits of Predictive Maintenance
AI-driven predictive maintenance can transform operations for businesses in several ways, let’s focus on the most important advantages.
Reduced Downtime
Organizations can benefit from identifying potential equipment issues before they occur, enabling proactive maintenance activities that will result in minimizing unplanned equipment downtime and production disruptions. According to a study by Deloitte, mentioned in the beginning of the article, predictive maintenance programs can result in 10-20% of increased equipment uptime and availability.
Increased Equipment Reliability
By continuously monitoring equipment health in real-time and predicting potential failures, predictive maintenance Machine Learning ensures that assets are maintained in optimal condition. According to UpKeep, roughly 10% of industrial equipment ever actually wears out, meaning a very large portion of mechanical failures are avoidable.
Optimized Maintenance Schedules
If Plant Engineering is to be believed, 18% of companies spend 40 hours or more per week on software maintenance activities. Predictive maintenance enables organizations to optimize maintenance schedule activities based on actual equipment condition and performance, rather than fixed intervals or reactive responses to failures. This optimization ensures that maintenance tasks are performed only when necessary, avoiding over-maintenance.
Cost Savings
Predictive maintenance reduces breakdowns, resulting in significant cost savings for organizations by preventing unplanned downtime, and emergency repairs, and optimizing maintenance resources. To be more precise, McKinsey believes that maintenance costs for a particular company can be reduced in a range from 18 to 25%.
Improved Safety
Proactively addressing equipment issues helps mitigate workplace safety risks associated with equipment failures. By preventing unexpected breakdowns, organizations create safer working environments for employees, reduce the likelihood of accidents, and ensure compliance with safety regulations. International Labor Organization reports around 340 million occupational accidents and 160 million victims of work-related illnesses annually, so saving lives and protecting health are major reasons for the implementation of a predictive maintenance Machine Learning solution.
Enhanced Asset Performance
Predictive maintenance allows organizations to identify opportunities for performance optimization and efficiency improvement. By monitoring equipment health and performance metrics, organizations can implement corrective actions to maximize asset performance, minimize energy consumption, and enhance overall operational efficiency.
There is an entire market for Asset Performance Management that is expected to be worth around USD 53.22 billion by 2032. Leaders in this market are already maximizing Machine Learning techniques to reach this astonishing growth.
Better Resource Allocation
With obtained insights into equipment health and maintenance needs, organizations can have better resource allocation and planning. According to a Gallup survey, disengaged employees cost U.S. companies between $450 billion to $550 billion annually in lost productivity.
Extended Equipment Lifespan
By proactively addressing issues and performing timely maintenance activities, it is possible to extend the lifespan of critical assets. This reduces the need for premature equipment replacements and capital expenditures, resulting in long-term cost savings and improved asset utilization. Averna states that the adoption of predictive maintenance can lead to a 70-75% decrease in breakdowns, resulting in a longer equipment lifespan.
Improved Customer Satisfaction
Predictive maintenance indirectly improves customer satisfaction by ensuring reliable and uninterrupted service delivery. By minimizing unplanned downtime and disruptions, organizations meet customer expectations for product availability, quality, and reliability, enhancing overall customer experience and loyalty.This benefit is universal for many industries, including Artificial Intelligence for Retail, where maintaining the health of assets can ensure the uninterrupted operations of POS terminals in stores.
Data-Driven Decision-Making
By utilizing Data Analytics and Machine Learning algorithms, organizations can generate actionable insights that enable informed decision-making. These methods, referred to as data-driven decision-making processes, allow organizations to make more accurate judgments in several areas:
- Maintenance Strategies: Data can indicate when equipment requires maintenance, helping to schedule these events effectively and minimize downtime.
- Equipment Investments: Insights derived from data trends can aid in deciding when to invest in new equipment or upgrade existing machinery, based on factors like performance metrics and lifetime value.
- Operational Priorities: By analyzing real-time data, organizations can immediately identify and address pressing operational issues, resulting in improved efficiency and productivity.
Overall, a data-driven approach offers a strategic advantage, driving better decisions and, subsequently, beneficial results.
We already discussed the importance of data-driven decisions in our article on the business impact of Big Data, so we invite you to read it for more information.
Predictive Maintenance Use Cases Across the Industries
Machine Learning algorithms are already changing the ways companies run their businesses. In this section, we will focus on some of the most interesting examples.
Manufacturing Industry
Predictive maintenance is perfect for the manufacturing industry, where it is widely used to monitor the health of manufacturing plant equipment such as CNC machines, robots, and conveyor belts.
In combination with IoT, it is possible to analyze sensor high dimensional data such as vibration, temperature, and pressure to detect anomalies and predict potential failures before they occur. In this scientific research, it is described how Machine Learning techniques are increasingly being used in the field of predictive maintenance to predict failures and calculate estimated Remaining Useful Life (RUL) of equipment. A case study proposed in this research experiment compares multiple combinations of feature engineering techniques and advanced ML models to find the most efficient techniques for prediction.
Do you want to learn more about the implementations of Artificial Intelligence and Machine Learning in Manufacturing?
Read our article to discover the most popular use cases.
Oil and Gas Industry
Critical equipment in oil refineries, offshore platforms, and pipelines can benefit from the modern maintenance approaches. Bitstrapped has spent 3 years working with a leading oil and gas consultancy to develop a dozen cloud-based predictive maintenance tools and applications for many leading oil and gas operators, including the Supermajors.
As a result:
- Organizations saw the time required to run analyses to determine maintenance decrease 10x, spanning 1 week, to a maximum of 20 minutes.
- In select cases, organizations saw as much as a 60% decrease in maintenance costs.
Transportation and Logistics Industry
Predictive maintenance can be used to monitor the health of vehicles in the Automotive industry, aircraft, and locomotives in transportation fleets, detect engine performance issues, and more. For example, Finland’s VR group manages a fleet of 1500 trains running on rails. They initiated the process of installing sensors to monitor fault points that can lead to failover. The VR group turned to SAAS Analytics to convert raw data into an actionable analytical report. As a result, this led to cutting down on the maintenance work by one-third, which is very cost-effective for the business.
Energy Industry (Power Generation)
Power generation facilities, including thermal power plants, hydroelectric dams, and wind farms can benefit from automatically adjusted maintenance activities. In a case study for the implementation of a predictive maintenance strategy for the power generating unit, it was proven that the PdM solution was able to predict 100% of failure devices monitored by the system, within a time horizon (3-17 hours).
Healthcare Industry
Medical facilities can benefit from monitoring and maintaining critical medical equipment such as MRI machines, CT scanners, and ventilators. Predictive maintenance models can analyze sensor data points and usage patterns to predict equipment failures or performance issues.
Hitachi analyzed three years’ worth of sensor data from 100 MRI systems and created a mechanism to investigate the cause of patterns that lead to device failures, as a result reducing the costly downtime caused by breakdowns by 16.3%.
Telecommunications Industry
Finally, predictive maintenance can be applied to telecommunications networks and infrastructure, including cell towers, fiber optic cables, and network switches. In a case study by Kortical, it is described how they helped spot 52% of failures before they happened for 22,000 mobile network towers throughout the UK.
Ready to explore the benefits of the technology for other industries?
Feel free to read our guide on Machine Learning and Artificial Intelligence in the Food Industry.
Future Trends in Predictive Maintenance
The estimations and predictions are looking very promising for the predictive maintenance solutions market. It is also possible to highlight the most prominent trends for the next few years. Rapid advancements in technology are driving the adoption of these trends.
As these technologies continue to evolve and mature, they become more accessible, affordable, and easier to implement for organizations of all sizes. Just like we see with AI for customer behavior analysis, predictive maintenance is set to achieve great heights.
Integration with IoT and Edge Computing
The number of connected IoT devices worldwide will be close to 30 billion by the end of 2030, according to Statista. Combined with the benefits of Edge Computing, which brings computational power closer to the data source, allowing for real-time analysis of sensor data at the edge of the network, this is a powerful driver for the evolution of predictive maintenance.
Predictive Maintenance as a Service
Predictive Maintenance as a Service (PMaaS) employs cloud computing infrastructure to render predictive maintenance functionalities as a service, following the subscription-based model. The scalability of both storage and computational resources in cloud platforms conveniently facilitate organizations in deploying and escalating predictive maintenance solutions.
Serhii Leleko
ML & AI Engineer at SPD Technology
“The core advantage of PMaaS solutions lies in their capacity to provide organizations with the opportunity to tap into the profound expertise and resources of third-party vendors. These vendors offer specialized analytics capabilities and maintenance algorithms, which empower organizations to concentrate on their primary operations while offloading the complexities of predictive maintenance.”
AR, VR, and Digital Twins
AR and VR are already having an impressive cross-industrial history, delivering significant value in several areas, including customer service, and their mainstream adoption is imminent. Advancements in Augmented Reality (AR), Virtual Reality (VR), and especially Digital Twins will have a positive impact on timely maintenance interventions as well. AR and VR technologies provide technicians with real-time guidance, visualization tools, and training simulations, allowing them to perform maintenance tasks more efficiently and accurately. Digital Twins, in turn, offers virtual replicas of physical assets or systems that mirror their real-time behavior and performance.
Conclusion
Predictive maintenance powered by Machine Learning offers a transformative approach to asset management across various industries, mostly Manufacturing, Energy, Transportation, and Telecommunications. The biggest value it brings is that a combination of collecting data and implementing advanced algorithms allows organizations to shift from reactive to proactive maintenance strategies, significantly reducing downtime, minimizing costly equipment failures, and optimizing operational efficiency.
We, at SPD Technology, are here to help you leverage innovative technologies to elevate your business. Our 600+ team of vetted professionals have experience in taking on the most challenging projects and are ready to transform your equipment maintenance processes with advanced predictive solutions!
FAQ
- What is Predictive Maintenance in Machine Learning?
Predictive maintenance in Machine Learning integrates Data Analytics and Machine Learning methodologies to ascertain potential equipment or machinery failures. The underlying principle is to undertake proactive maintenance based on this predictive model, significantly reducing downtime and preventing expensive malfunctions.
- How to Implement Machine Learning for Predictive Maintenance?
The typical approach includes:
- Procuring historical data from sensor readings, IoT devices, or any relevant sources.
- Refining the data and configuring it for subsequent analysis.
- Pinpointing the salient features contributing to equipment failure.
- Selecting suitable Machine Learning algorithms.
- Training the designated model using the historical data.
- Assessing the efficacy of the trained model with metrics like accuracy, precision, recall, and F1-score.
- Deploying the trained model into a production environment. This model thereafter facilitates continuous monitoring of incoming unseen data, making real-time prediction feasible when maintenance is necessitated.
- How Does Machine Learning Help Telecom and Network Predictive Maintenance?
For these sectors, Machine Learning can contribute extensively by:
- Through network performance data analysis, Machine Learning models can preemptively identify potential faults or service hindrances.
- Machine Learning algorithms can unearth abnormal phenomena in network traffic and equipment performance, thus flagging probable issues deserving attention.
- Employing historical data analysis, Machine Learning can detect patterned network usage and predict future demand. This predictive capability empowers telecom companies to enhance resource allocation efficiency.
- Machine Learning can anticipate the failure of network components or infrastructure, leveraging historical equipment performance data and environmental factors.