The cornerstone of modern maintenance may be predictive maintenance, but preventive maintenance is still very much in use, especially in industries where errors can be fatal. Whichever approach is used, data plays an essential part. Better  use of data results in organisations being able to determine the state of the process and condition of equipment in order to estimate when maintenance should be performed. The goal? Fix potential defects before they result in failure or effect on process performance. 

On-time maintenance is made possible by the integration of data utilization capabilities. At the end of the day, successful implementation is dependent not only on the organisation’s ability to turn data into accurate information, but also to further leverage the information to implement an efficient, scalable maintenance plan. Below are some tips, tricks and first-hand examples of how you can implement new data-driven strategies to enhance productivity, profitability and efficiency gains.

Leverage modern data analytics tools

Modern software tools can help you and your team analyze available data and translate it into accurate information that will help improve operations. They combine online data from various sources, including maintenance, condition monitoring, process control (DCS) and quality control (QCS) systems, to enable efficient data analysis. The data utilization can be visual, smooth and powerful, leading to enhanced production and easier maintenance development.

For companies pointed in this direction, the best case scenario is that all digital information is seamlessly integrated and users have a clear, overall picture of the process as a basis for better decision-making. 

Five practical examples of data-driven maintenance

As you review these examples, keep in mind that the opportunities are limitless when high-quality data is combined with intelligent calculations and algorithms. For example, it is possible to estimate optimal maintenance timings, calculate sub-process performances or assess runnability risks.

#1: Begin with an equipment-level view

Often, when assessing the need for maintenance, it is worth going back to basics and begin by visualizing the data. Take a pump, for example. Collect and monitor the most critical measurements regarding the equipment’s operating condition (speed, current, vibration, temperature, etc.). Learn from that if the equipment behavior is normal.

#2: Start recognizing patterns

Study the dynamics of the process. You may notice that the relationship between, for example, the pump’s speed and current follows a certain curve. A deviation from that should potentially lead to an action by maintenance. 

#3: Implement an SPC approach

The Statistical Process Control (SPC) data processing method has proven its power in several process industry areas. SPC is often used within Six Sigma initiatives to provide a factual base for the measurement type of methodology of process improvement, whether such a process is in relation to a production process or a service process. 

SPC limits are also regularly utilized by maintenance teams. Deviation from the limits should potentially trigger an event-driven maintenance action.

#4: Compare process condition changes

Process conditions such as equipment setup, process control strategy, used raw materials and required properties of the final product are constantly changing. Studying the process changes provides a great way to learn about the process. On Trimble’s Wedge, for example, this can be done with the annotations or notifications to contextualize data. 

The maintenance team might want to compare process variables before and after maintenance activities such as pump or valve replacement. For example, what changed in the data when we replaced a pump, and what can we learn from that? Is it possible to use this new information to schedule the next pump change more accurately?

#5: Study control loop performance

A modern production plant has a production control system and thousands of control loops that ensure production efficiency. Control loop performance is one of the automation maintenance team’s biggest headaches because poorly functioning control loops can cause serious deviations in product quality or profitability. 

In order to evaluate control loop performance, combine control loop set points, controller outputs, measurements and state data. By analyzing the averages, deviations, maximums and minimums of the factors, it is possible to determine bottlenecks, dead loops, broken measurements and deviations easily.

In the end, remember that it’s not about how much data is collected, it’s what you do with the data. While most modern production operations generate significant amounts of data, the most successful ones are leveraging it to drive the business forward.

 

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Running a complex large-scale industrial operation requires a vast amount of expertise and an equal amount of good judgment, especially when it comes to deciding which functions to fulfill in-house and those best to outsource. In the end, it is not possible to excel in every function on your own – recruiting the best talents, developing knowledge, systems and processes in every area is too great a challenge to any company. Choices must be made.

While each sector, business and company culture will vary, a reliable guiding principle to follow when considering these choices is: Focus on your core. Keep all functions that are core to your business in-house as these operational areas are vital to ensuring your organization’s competitive advantage. For example if you’re running a paper or pulp plant, don’t hand over control of any functions directly related to managing the final product. Non-core functions, however, can be considered – and, indeed, may be executed more efficiently when outsourced. Now, let’s consider some of the key questions that come up for executives as they analyze these core decisions.

How do I evaluate which functions are core to the operation?

Assuming your company has taken the strategic decision to keep manufacturing in-house, you’ve already taken the first step. You most likely have several performance indicators, like the OEE (Overall Equipment Effectiveness), in use to help track your operation’s ability to compete. As long as your R&D, process engineering and quality departments function seamlessly and in tandem in order to minimize costs, optimize the production performance, improve quality or add flexibility and agility, your company will maintain a good position. So,as a guiding principle, consider any processes and functions that have a direct impact on your key performance indicators as sacred ones, i.e. your core functions. 

In terms of performance indicators, keep in mind that being in the driver’s seat will enable successful understanding, accurate measurement and your ability to effectively contribute to an efficient operation. Understanding and measuring are easy – but the latter can be more challenging as it requires serious process and quality engineering.

For example, take any continuous development framework and you will notice that the first step of the process will be intertwined with information gathering, data collection or data/process analysis. The same applies to developing performance – the process needs to be structured and data-driven right from the beginning all the way to communicating and implementing the findings. The talented and curious people in charge of that process, i.e. engineers, can be considered the soul and incarnation of your competitive edge.

How does Trimble support industrial plants in making these important decisions?

At Trimble, we work in the field of process analysis and diagnostics. We have supported many companies contemplating in-house and outsourced approaches to process and quality development. While some companies proudly maintain and develop their process knowledge in order to improve the performance, there are others that have chosen to outsource troubleshooting and process development. In our experience it is the first group with the brightest, and most sustainable, future.

The second group are essentially asset owners. This includes industrial plants that opt to outsource their efficiency development to automation and algorithms. They implement cutting-edge technology and let it take care of challenging tasks such as optimizing the process. There is some merit to this rationale, however, a system is only as good as its configuration and embedded models. Furthermore, all systems and equipment are available to everyone. 

In the end, the way to make a true impact comes down to how you use and configure the best available technology. For today’s most successful operators, we have discovered that this decision should be based on protecting – in-house – those technologies, processes and proprietary intel that contains the core knowledge of running your business. 

 

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Our sales team is full of data analysis experts ready to help you. Learn more about Wedge industrial data analytics tool.