We live in a world full of mechanical components and machines that we use day by day. All machines run to failure at some point. An unexpected breakage of domestic appliances affects your mood or your monthly budget, while any failure in manufacturing can cost millions of dollars.
Every company needs to control the quality of equipment and organize regular maintenance. It can reduce the probability of failures and possible losses for a company. Among the strategies to preempt these failures, adopting predictive maintenance solutions and harnessing machine learning for predictive maintenance stand out as pivotal.
This article may be relevant to Data Analysts and specialists within the Machinery industry.
Predictive maintenance is a technique of continuous monitoring of equipment performance and defining its condition. Such monitoring during normal operations helps to detect possible defects and successfully predict failure in advance. The main goal of predictive maintenance is to reduce unplanned glitches or downtime of equipment.
Predictive maintenance makes it possible to conduct timely technical support. This approach involves periodic inspections and repairs of defects with the goal of avoiding equipment malfunction or failure.
As for any data analytics, you will need historical data and ML algorithms to organize predictive maintenance. The success of predictive maintenance models depends on three main components: having the right data available, framing the problem appropriately, and evaluating the predictions properly.
Predictive maintenance can be done by utilizing the process of anomaly detection. Algorithms show you any abnormal behavior of the equipment, thus, you will be able to detect and resolve any encountered issues. As well as monitoring the ongoing processes, you can identify if there's a possibility of future failures.
Here, a comprehensive understanding of equipment behavior metrics is pivotal. As a result, companies can effectively forecast and prototype their equipment's future condition through predictive maintenance technologies
The Remaining useful lifetime (RUL) is the length of time equipment is predicted to operate before it requires repair or replacement. Regression models for the RUL require available static and historical data with labeled events.
The data that can be used to determine RUL is the following:
As a result, you will have information about how many days or cycles are left before the system fails.
If you decide to create a model that can predict lifetimes very accurately, it can be very challenging and time-consuming. However, usually, you don't need to be very precise when it comes to failure prediction. The information that "the equipment is going to fail soon" often is enough. As with the regression models for RUL, you will need both static and historical data with all events labeled. In fact, regression and classification models are pretty similar, but mostly differs on:
As a result of such analysis, you will know if a machine fails in the next period of time.
In some cases, analysis of the failures doesn't work. In lots of different industries, the is very little information about failures or no examples at all. For example, you can't afford to collect data about airplanes or railways crashes, as it's a matter of not only money but potentially human lives. For such cases, you need to monitor the behavior of the vehicle or a machine to figure out if the shown behavior is normal or not.
For such analysis, you will need static and historical data, but there may be labels that are unknown or too few failure events were observed or there are too many types of failure.
It is possible to define what normal behavior is and the difference between current and "normal" behavior is related to degradation leading to failure.
The anomaly detection model is very controversial because of its generality. The model should be able to detect all kinds of failure, even in cases with no previous information about them. At the same time, abnormal behavior doesn't necessarily fail. In cases, where it can lead to failure, the model doesn't provide the information about possible time frames when it might occur.
The analysis of an anomaly detection model is also challenging because of the lack of labeled data. When no labeled data is available, the model is usually made accessible and domain experts provide feedback on the quality of its anomaly flagging ability.
A survival model evaluates the probability of failure for a specific type of equipment with static features, as well as it shows the impact of certain features on a lifetime. It provides, therefore, estimation for a type of machine with similar characteristics.
This strategy gives an analysis of how the risk of failure can change in time with the set characteristics.
While predictive maintenance machine learning methods and predictive maintenance software are revolutionizing the manufacturing sector, they come with their own set of challenges. It's essential for businesses to recognize these potential hurdles to fully harness the power of predictive maintenance.
Here are some of the key challenges to consider:
Data Volume and Quality: The success of predictive maintenance heavily relies on the quality and volume of data. Ensuring that data is accurate, relevant, and consistently available can pose a challenge.
Defining "Normal" Behavior: With myriad predictive maintenance tools available, determining the standard or "normal" behavior of equipment can be daunting, especially with evolving machine standards and practices.
Complex Implementation: The line between predictive maintenance vs preventive maintenance can sometimes blur. Determining which approach to prioritize and integrating them harmoniously can prove complex.
High Initial Costs: Investment in cutting-edge predictive maintenance technologies and software solutions can be substantial. While the returns are significant in the long run, the initial outlay can deter some businesses.
Training and Skill Set: Understanding and leveraging predictive maintenance solutions requires a specialized skill set. Training personnel and ensuring they are up-to-date with the latest methodologies can be a continuous challenge.
Interpreting Predictive Data: While predictive maintenance machine learning models can provide a wealth of data, interpreting this data correctly and making informed decisions based on it is crucial. Misinterpretations can lead to erroneous actions, defeating the purpose of predictive maintenance.
In conclusion, while the predictive maintenance definition underscores its vast benefits, addressing these challenges head-on is crucial for businesses to maximize their potential.
Training and Skill Set: Understanding and leveraging predictive maintenance solutions requires a specialized skill set. Training personnel and ensuring they are up-to-date with the latest methodologies can be a continuous challenge.
Interpreting Predictive Data: While predictive maintenance machine learning models can provide a wealth of data, interpreting this data correctly and making informed decisions based on it is crucial. Misinterpretations can lead to erroneous actions, defeating the purpose of predictive maintenance.
In conclusion, while the predictive maintenance definition underscores its vast benefits, addressing these challenges head-on is crucial for businesses to maximize their potential.
Predictive maintenance is elevating manufacturers to new operational heights. By blending advanced technology with deep learning insights it not only optimizes machinery function but also fortifies long-term business investments. Let's explore its multifaceted benefits:
1. Basis of Action:
Predictive Maintenance (PdM): Operates based on real-time equipment conditions using data-driven insights. It waits for a specific set of criteria to be met before action is taken.
Preventive Maintenance (PM): Follows a pre-determined schedule, either based on time intervals or equipment usage.
2. Cost Efficiency:
PdM: Can be more cost-effective in the long run as it targets specific issues, minimizing wastage of resources.
PM: This can sometimes lead to over-maintenance, resulting in potentially unnecessary costs.
3. Risk of Equipment Failure:
PdM: Reduces the risk of unexpected failures by constantly monitoring equipment health.
PM: Mitigates the risk based on historical data, but there's still a possibility of unpredicted failures.
4. Implementation Complexity:
PdM: Requires sophisticated tools, sensors, and data analytics platforms for accurate predictions.
PM: Typically simpler to implement, relying on schedules derived from manufacturer recommendations or past experiences.
5. Flexibility:
PdM: Offers more flexibility, adjusting maintenance activities based on actual equipment conditions.
PM: Has less flexibility due to its fixed maintenance schedule.
In essence, while preventive maintenance sticks to a set routine, predictive maintenance adjusts its approach based on real-time data analytics, ensuring machinery operates optimally for longer periods
One of the greatest examples of using predictive maintenance is Tesla. Their cars are known as one of the most innovative and reliable, and they keep improving their products. Tesla has recently implemented a new initiative: Tesla cars can now self-diagnose internal problems and order replacement parts from the diagnostic center. A car monitors conditions of all the elements, analyses it, and shows a notification of an issue with the car's power conversion system. The car automatically places an order for replacement parts to be delivered to a Tesla service center, where the owner's car would be repaired.
Implementation of a machine learning approach in predictive maintenance is a complex process that requires a lot of resources. Of course, companies need to collect static and historical data on the operations and monitor their equipment. Once a company has enough data, it's still quite challenging to develop a good ML approach from scratch: it requires time, good expertise, and a lot of pre-processing and feature engineering to determine the failure patterns.
There are two possible ways to implement predictive maintenance using artificial intelligence and machine learning:
Sometimes it's difficult to find a proper tool and not to waste a lot of time. If you're interested in predictive maintenance implementation, you can contact the Datrics team. At Datrics.ai we're working on a platform that helps with analytical routines. Our experienced team of Data scientists will help you with your needs.
With the datrics.ai platform, you can easily analyze big chunks of data and streamline the process of equipment maintenance without any prior technical knowledge.
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