How do you use ensemble and hybrid methods for predictive maintenance?

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Predictive maintenance is the practice of using data-driven models to anticipate and prevent equipment failures, reduce downtime, and optimize maintenance schedules. However, building accurate and reliable predictive models is not an easy task, as it requires dealing with complex and noisy data, selecting appropriate features and algorithms, and evaluating the performance and robustness of the models. In this article, you will learn how to use ensemble and hybrid methods for predictive maintenance, which are techniques that combine multiple models or approaches to improve the overall results.

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1 What are ensemble methods?

Ensemble methods are techniques that use multiple models, often of different types, to make predictions or classifications based on the same data. The idea is that by combining the strengths and compensating the weaknesses of different models, the ensemble can achieve better accuracy, stability, and generalization than any single model. There are different ways to create and combine ensemble models, such as bagging, boosting, stacking, and voting. For example, bagging involves training multiple models on different subsets of the data and averaging their outputs, while boosting involves sequentially training models on the data weighted by the errors of the previous models and adding their outputs.

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Carlos E. Torres

CEO @ Power-MI | Predictive Maintenance, Mechatronics, Executive Leadership
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The only application of ensemble methods in Predictive Maintenance is to calculate the Remaining Useful Life of assets. Think of ensemble methods like putting together a superhero team to predict a machine's health. Each hero (model) has unique powers (insights from different data patterns). When they team up, they cover each other's weaknesses, making the prediction of when a machine might need a major repair (its Remaining Useful Life) more accurate and reliable, even when data is messy or incomplete.

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John Q. Todd
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The huge first step is choosing a handful of models to start with. Unless you have a clear understanding of the limits and true outputs of the model, given your data sources, lots of time can be wasted. Even worse if you begin to make decisions with the model results without foundational understanding of the model.

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2 What are hybrid methods?

Hybrid methods are techniques that use multiple approaches, often of different paradigms, to solve a problem or achieve a goal. The idea is that by integrating the advantages and overcoming the limitations of different approaches, the hybrid can achieve better performance, flexibility, and scalability than any single approach. There are different ways to design and implement hybrid methods, such as combining data-driven and physics-based models, combining supervised and unsupervised learning, or combining machine learning and optimization. For example, a hybrid method can use a data-driven model to predict the remaining useful life of a component and an optimization method to determine the optimal maintenance schedule.

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Carlos E. Torres

CEO @ Power-MI | Predictive Maintenance, Mechatronics, Executive Leadership
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Using machine learning in predictive maintenance is like creating a smart maintenance assistant. This assistant learns from data collected from machines and combines it with rules based on how machines are supposed to work. So, it's not just guessing when a machine might need fixing; it's making a well-informed prediction. This way, you get a heads-up on the best time to repair or service the machine before any problems actually happen.

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John Q. Todd
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Optimization models require rather clean data and lots of computing power to be useful. Make sure the underlying computing environment has the capacity to run the software tools. Otherwise, it may take longer to get optimization results than it does to just eyeball the schedule and push it out to the field crews.

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3 Why use ensemble and hybrid methods for predictive maintenance?

Ensemble and hybrid methods can offer several advantages for predictive maintenance, such as enhanced accuracy and reliability of predictions or classifications when the data is noisy, imbalanced, or heterogeneous. Furthermore, these methods can improve the robustness and generalization of the models when the data is scarce, dynamic, or uncertain. Additionally, they can exploit complementary information and knowledge from different models or approaches when the problem is complex, multidimensional, or nonlinear. Finally, they can increase the flexibility and scalability of the solutions when the problem is diverse, evolving, or context-dependent.

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Christoph Netsch

I help businesses build machines that self-monitor, self-diagnose & self-optimize.
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A widely used task in predictive maintenance is anomaly detection - algorithms are used to learn how a machine ought to behave, under specific operational circumstances, so an alarm can be raised once the machine's behavior starts deviating from this. There are many types of statistical and ML algorithms for this. Some are designed to input timeseries data directly from a machine's intrinsic sensors, others for the tabular data, that you get i.e. when precomputing certain statistical features of a machine cycle. Some have proven to work well for point anomalies, others for contextual anomalies. Since there is not one silver bullet, creating an ensemble of multiple algorithms that compliment each other tends to increase robustness.

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John Q. Todd
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Leveraging complementary models and results can be very insightful for decision making. It can easily happen where two models provide you two "different," results. That can be frustrating, but it can also be very helpful.

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Carlos E. Torres

CEO @ Power-MI | Predictive Maintenance, Mechatronics, Executive Leadership
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Hybrid methods in predictive maintenance mix learning from real-world data and engineering principles to get a fuller picture of how machines are doing. In the aerospace industry, this approach has been really helpful in making aircraft parts last longer. By using past data and simulations to predict when a jet engine might break down, we can plan maintenance more wisely. However, as far as I've seen, there are not practical applications in industry.

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4 How to use ensemble and hybrid methods for predictive maintenance?

The choice and implementation of ensemble and hybrid methods for predictive maintenance depend on several factors, such as data availability and quality, problem definition, model objectives, and evaluation criteria. To guide the process, you should define the problem and data, select models or approaches to address the problem and data, and combine the models or approaches. When defining the problem and data, identify the type and scope of the predictive maintenance problem and collect relevant data from the equipment or system. When selecting models or approaches, consider advantages and disadvantages of different types of models or approaches. When combining the models or approaches, determine parameters and methods that can control and optimize the combination. Finally, test and validate the ensemble or hybrid based on accuracy, stability, generalization, or robustness.

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John Q. Todd
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Always good to perform a "gut check." Do these results or predictions make sense? If the model tells us that a failure will probably not occur in the next 1500 years, is that result useful?

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Carlos E. Torres

CEO @ Power-MI | Predictive Maintenance, Mechatronics, Executive Leadership
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To use ensemble and hybrid methods for predictive maintenance, you'd combine multiple predictive models to improve accuracy. For example, ensemble methods might integrate different algorithms like decision trees and neural networks to analyze vibration data from machinery. This combination helps identify potential failures more reliably by cross-verifying the findings of each model. Hybrid methods might blend data-driven models with physics-based simulations to predict when a component like a turbine blade might fail under varying conditions. This approach leverages both historical data and theoretical insights, ensuring maintenance schedules are both data-informed and contextually relevant, enhancing machine uptime and efficiency.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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John Q. Todd
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Beware of throwing or filtering data away. Sometime the value of the data lying in the tails of the distribution can lead to insight you didn't have before. Outliers can easily be those gems of data that make a difference between success and failure.

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Carlos E. Torres

CEO @ Power-MI | Predictive Maintenance, Mechatronics, Executive Leadership
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While hybrid methods for predictive maintenance are promising, their practical application is not yet widespread in industry. These advanced techniques, combining multiple analytical approaches, are mostly utilized in sectors like aerospace but face challenges in broader adoption due to integration complexities, costs, and organizational change resistance. As technology evolves, we anticipate increased implementation across various industries.

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How do you use ensemble and hybrid methods for predictive maintenance?

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1 What are ensemble methods?

2 What are hybrid methods?

3 Why use ensemble and hybrid methods for predictive maintenance?

4 How to use ensemble and hybrid methods for predictive maintenance?

5 Here’s what else to consider

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