Prescriptive vs. Predictive Maintenance: Bridging the Gap with RAG Models

3 min read ● Silk Team

For years, the industrial operation world has been using predictive maintenance (PdM). PdM uses sensor data and machine learning to predict when a component will be down. A prediction is only a heads up. What really matters in terms of operational performance is prescriptive maintenance (RxM). RxM goes further than the heads up and actually gives you a specific, data driven answer to what you need to do.

There has always been one “last mile” of the decision making process when it comes to maintenance. That last mile is taking a vibration alert and turning it into a step by step repair guide. That is where the use of retrieval-augmented generation (RAG) is revolutionizing the way we look at maintenance.

Maintenance: An Evolution From “When” To “What Now”

To see how artificial intelligence is impacting the way we think about and operate our manufacturing floors, we first have to differentiate between the two approaches:

• Predictive Maintenance (PdM):Is an answer to the question “when will this fail?” Uses time series data from sensors (temperature, pressure, vibration) to detect anomalies. Saves money by stopping “run to failure” failures, but still leaves it up to the human expert to determine the root cause of the problem and to find the solution.
• Prescriptive Maintenance (RxM):Is an answer to the question “what do I do about it?” Takes the predicted failure, along with all the relevant business logic, previous maintenance records and technical information and creates a recommendation of what to do next — such as slow the machine down to extend the life of the machine until the next scheduled shutdown or order a specific part for an immediate exchange.

The most significant advantage of prescriptive maintenance is the reduction in mean time to repair (MTTR) as it removes the uncertainty associated with identifying the root cause of a failure.

The Role of RAG: The Missing Link In Decision Support Systems

One of the major barriers to implementing prescriptive maintenance is the sheer amount of unstructured data. While sensors produce structured numbers representing the state of the machine, the answers to what needs to be done are found in unstructured documents like pdf files, legacy manuals and years worth of written notes from technician maintenance logs.

Retrieval-Augmented Generation (RAG) fills this gap. Rather than having a generalized AI model make up a repair procedure based on nothing other than the raw data, a RAG powered system does the following:

• Receives the specific technical manual and maintenance history for the specific serial number of the machine that is failing
• Enhances the raw sensor alert data with this contextual information
• Generates a natural language instruction for the technician

RAG in The Maintenance Process

Consider a CNC machine showing a 15% rise in spindle temperature.

• The Predictive Layer:Triggers an alert: “Failure of spindle expected within 48 hours”.
• The RAG Layer:Scans the 400 page manufacturer manual, cross references it with a very similar failure from three years ago in the company’s internal logs, and checks to see if there are spare bearings in stock.
• The Prescriptive Output:“Spindle overheating. According to section 4.2 of the manual, and according to the 2022 incident report, tighten the spindle bolt mounting to 50 Nm. Part #882 is in stock room B.”

Benefits of Using RAG to Drive Your Maintenance Strategy

Three primary benefits result from integrating RAG technology into your maintenance program:

• Documentation Silo Elimination:Technicians no longer spend 30% of their day searching for manuals. The AI puts the manual in front of the problem.
• Root Cause Analysis Automation:Through analysis of multiple sources of data including images of wear or audio of grinding motors, RAG models can determine why a failure occurred, not just that it did.
• Knowledge Retention:With senior technicians retiring, their “tribal knowledge” is typically lost. RAG models can assimilate previous repair notes so that a junior technician in 2026 will have the same insight as a 30 year experienced technician.

Conclusion: Moving Toward Autonomous Decision Making and Strategic Reliability

The transition from predictive to prescriptive maintenance is not just an upgrade in software, it is an evolution toward autonomous decision support. By utilizing RAG models, organizations can convert “noise” in the form of raw data into a clearly defined list of instructions for each maintenance task, thus ensuring that each maintenance action taken is the best option available to ensure both the longevity of the equipment and the organization’s financial health.