From Reactive to Predictive: How IIoT Is Transforming Maintenance Strategies

From Reactive to Predictive: How IIoT Is Transforming Maintenance Strategies

Industrial maintenance…a headache? Yes. Thief of time? Absolutely. Reputation killer? It has its moments! These are the results of relying on a reactive approach to industrial maintenance. Reacting when equipment failures occur repeatedly without warning leads to operational downtime, production delays, and unplanned expenses that come directly out of a company’s profit margins.

It’s been a long time coming, but we finally reached a pivotal moment of change.

Now businesses can shift from a traditional reactive maintenance model to a smarter, more efficient approach—Predictive Maintenance (PdM)—by leveraging data and the Industrial Internet of Things (IIoT).

The Power of Predictive Maintenance: Why IIoT Matters

What if we could look into the future and see when our equipment begins to fail? What if we could see data that describes real-time equipment performance?

With the rise of IIoT, Artificial Intelligence (AI), and Machine Learning (ML), we have made this a reality.

Time Series Data as the Foundation

Let’s start by learning how we’ve moved from a predictive maintenance approach to a proactive one. It all begins with time series data!

Time series data isn’t quite as complex as it sounds. It describes the continuous collection of data over a specific period. But how does this help with maintenance scheduling?

IIoT devices, which include things like equipment sensors, can continuously collect data on crucial performance metrics like pressure, temperature, and vibrations—basically, anything that signals a change in how a machine functions. By analyzing continuous data points over time, you can spot patterns and anomalies that would otherwise go unnoticed, many of which signal impending equipment failures.

You gain the insight to make better maintenance decisions by understanding how your equipment performs based on those indicators.

Machine Learning (ML) and Artificial Intelligence (AI)

While time series data certainly provides a high level of value and insight into equipment performance, it’s only the foundation you need to make highly informed decisions. That’s where AI and ML come into play.

AI and ML technologies process vast amounts of data and identify subtle trends or anomalies that a human might miss. We can predict when a machine by applying these AI and ML algorithms to historical performance data before the equipment, part, or entire system begins to degrade.

A study by ServiceMax found that approximately 82% of businesses have experienced unexpected downtime in the last three years. While not surprising, this is a clear indicator that better maintenance strategies are needed, regardless of the type of business or industry.

To build on that hypothesis, another Deloitte study suggests that businesses implementing predictive maintenance lower their maintenance costs by 25% on average.

There’s a clear correlation between downtime and maintenance costs, and businesses that operate using machinery and equipment should not overlook the consequences.

Cost Savings & Efficiency Gains

Leveraging IIoT-powered predictive maintenance strategies is a no-brainer for businesses looking to achieve significant savings on maintenance.

Preventing equipment breakdowns or failures reduces repairs and limits downtime, ensuring that maintenance work happens only when necessary. It also helps optimize the use of resources, labor, and often costly materials.

In fact, PdM strategies can boost labor productivity by up to 20%, so the upfront investment into these technologies can save businesses a lot of money in the long run.

Real-World Applications: IIoT in Manufacturing, Energy, and More

Predictive maintenance, powered by IIoT, transforms how nearly every industry operates. While each sector benefits in its unique way, a level of consistency remains across each domain.

Energy Sector

The energy sector is a big player that benefits from predictive maintenance. Energy producers must ensure their systems perform optimally, especially those involved with wind and solar production.

Wind producers can track system performance and other environmental parameters that affect output with IIoT-enabled sensors installed on turbines. Users can analyze data and make better predictions on when equipment operates inefficiently since AI and ML can analyze data over time.

For context, research shows that generators account for more than 5% of wind turbine failures, resulting in nearly 10% of overall downtime for each system. Predicting when a generator might fail could significantly reduce that downtime.

Manufacturing

Manufacturing is another sector that stands to benefit significantly from predictive maintenance. When paired with AI and ML technologies, sensors on manufacturing equipment can track performance metrics like motor speed, component degradation, and energy use and alert facility managers when something isn’t operating optimally.

Proactive maintenance in manufacturing can save companies a ton of money. According to GE, they reduced maintenance costs by nearly 30% annually, providing a significant return on their investment year after year.

Agriculture

The agricultural industry can also benefit greatly from predictive maintenance, though it might not be as apparent as the industries listed above. Agricultural equipment like crop harvesters, irrigation systems, and tractors are pivotal for food production and contain sensors that monitor soil moisture, assess crop health, identify diseases, and more.

Without seeing how their crops and fields are performing, farmers cannot safeguard themselves from the consequences.

Data Management and Maintenance: Challenges & Considerations

As industries begin to adopt predictive maintenance strategies, there will undoubtedly be challenges and considerations to make. There are vast amounts of data involved with PdM, so understanding what’s involved is crucial to successful implementation.

Data Overload & Storage

One of the first things to consider is how much data predictive maintenance involves. IIoT devices generate a ton of data, and organizations may struggle to develop actionable insights without an efficient way to manage the flow of information.

Interoperability & Security

Another critical consideration is the interoperability of IIoT devices and the security of the data being processed. IIoT devices often come from different vendors, which can use various communication protocols.

Each device must work seamlessly with one another to gain real value through the data.

Aside from the interoperability between devices, data security is also important. Cyber threats and data leaks could cause severe problems without a high level of security, with each business or industry facing different concerns.

Scaling Predictive Maintenance

Scalability is important in nearly every aspect of business, and scaling predictive maintenance strategies across locations or organizations can be challenging. These technologies must scale quickly and efficiently to effectively develop solutions for various uses and environments with PdM.

How InfluxDB Can Help

The ability to process and analyze the expansive amounts of time series data produced by IIoT sensors and devices is a key element of successfully implementing predictive maintenance strategies. As part of the InfluxDB 3 suite from InfluxData, InfluxDB 3 is proven to handle high volumes of data at scale, eliminating challenges commonly seen with predictive maintenance strategies. It helps businesses effectively manage continuous data over time using real-time data ingestion and querying capabilities, ultimately turning raw IIoT data into actionable intelligence.

In short, InfluxDB 3 offers:

• Efficient Data Ingestion: Collect and manage vast streams of IIoT and AI-driven data seamlessly.
• Real-Time Analytics: Gain actionable insights instantly to optimize predictive maintenance, energy management, and more.
• Scalability: Scale effortlessly to meet the growing demands of your business without compromising performance.
• Smarter Decision-Making: Make data-driven decisions faster, reducing costs and boosting operational efficiency.

Learn more about InfluxDB 3 and see how it can transform your business today.

Sponsored by InfluxData

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