How To Use Predictive Maintenance For Battery Safety

Due to the rapid emergence of renewable and distributed generation, the distribution and use of electricity are becoming more strained. To address this issue, various government agencies and organizations are working toward developing standards and practices related to the design, engineering, and commissioning of BESS systems.

Although it may be difficult to predict the future failure of a battery, using predictive maintenance can help prevent accidents and breakdowns before they happen. This method can help prevent costly repairs and prolong the life of your batteries. With the use of predictive maintenance, companies can determine the safest way to use a lithium-ion battery for industrial equipment.

What is Predictive Maintenance?

This process involves analyzing various data sources to determine the exact moment when a piece of equipment stops working. This technique has been used since before the 1990s, and it has continued to gain traction due to how many tools and techniques related to data analytics are popping up.

Predictive maintenance is accomplished by continuously monitoring an asset’s condition to ensure that it’s performing at its optimal level. A battery management system then collects data related to the various issues that affect an asset.

Predictive maintenance is designed to identify the ideal time to perform regular maintenance on an asset. It can help extend the life of the battery and maintain its capacity at its optimal level.

Understanding How Battery Safety Ties in With Predictive Maintenance

With predictive maintenance, an organization can precisely determine the right time to perform battery maintenance or replace a battery. It can then analyze the various data points collected by the management system to come up with accurate predictions.

With the help of IoT applications, you have a better chance of predicting the likelihood of a disaster or an incident. These applications can be combined with machine learning (ML) and advanced cloud technology.

Predictive vs. Preventative

The difference between predictive maintenance and preventive maintenance is when the process gets carried out and how data is gathered.

In preventive maintenance, the goal is to perform the necessary inspections and repairs regardless of the equipment’s use, and the collected information is mainly based on historical records. Contrastingly, predictive maintenance aims to perform the necessary repairs whenever needed, and the data in predictive maintenance is collected from real-time data.

Although preventive maintenance can be more affordable than predictive maintenance, it may still cost more in the long run. For instance, in preventive maintenance, the goal is to perform the necessary repairs regardless of the equipment’s use. On the other hand, when using predictive maintenance, the goal is to perform the necessary repairs whenever needed.

Benefits of Using Predictive Maintenance Programs

Overcharge and discharge can affect a battery’s life and capacity. With predictive maintenance, it can be predicted before a device needs to be replaced. This method can also extend the lifespan of a battery by identifying its condition before it needs to be changed.

A battery management system can help you determine when you need to perform repairs or replace a battery. This method removes the need to follow the manufacturer’s instructions regarding regular maintenance.

Studies have shown that predictive maintenance can reduce the cost of repairs and improve the productivity of an organization by up to 40%. This method can also help prevent costly breakdowns and accidents.

Despite the various advantages of predictive maintenance, it can still be considered a waste of time and money. This method should be implemented by every organization to improve its efficiency and prevent costly accidents.

Implementing Predictive Maintenance

Although predictive maintenance is commonly used for regular maintenance, it shouldn’t be considered a waste of time and money. Before implementing this method, it’s important that organizations thoroughly study its various advantages.

1. Find which critical assets need predictive maintenance.

2. Create a historical database for the critical assets.

3. Asses any current maintenance programs.

4. Make failure predictions by identifying failure modes.

5. Determine which technology to use for predictive maintenance.

6. Explain to stakeholders the need for predictive maintenance and ask them to buy in.

Final Thoughts

Before implementing predictive maintenance, employers need to consider the learning curve. The complexity of implementing IoT solutions can cause this.

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