Predictive Maintenance for Sewer Systems Using Smart Data

Key Takeaways

Predictive maintenance uses sewer system data to prioritize cleaning, inspection, repair, and rehabilitation before failures occur.
Smart data can come from acoustic assessments, level sensors, I&I monitoring, digital twins, CCTV records, work orders, and historical maintenance logs.
The best programs combine data sources rather than relying on one inspection method for every asset.
Predictive maintenance helps utilities move away from blanket cleaning schedules and toward condition-based maintenance.
RH Borden’s acoustic assessment and digital twin services are built around field-scale condition data, then turning that data into practical maintenance decisions.
Good data governance matters. A messy dataset can produce a messy maintenance plan.
The right starting point is usually a focused pilot, not a systemwide overhaul on day one.

Predictive maintenance for sewer systems uses field data, smart sensors, acoustic screening, digital records, and risk scoring to help wastewater teams find problems before they become emergencies. Instead of cleaning every line on a calendar or waiting for a backup, utilities can use condition data to decide which assets need attention first.

The practical goal is simple: fewer surprises, better use of crew time, and a maintenance plan that reflects what is actually happening underground. The EPA notes that sanitary sewer overflows can be caused by blockages, line breaks, sewer defects, power failures, and other system issues, which is why early visibility matters.

Smart data does not replace experienced operators. It gives them a better map. When collection system teams can see pipe condition, manhole degradation, wet weather response, and blockage risk in one planning process, predictive maintenance becomes less of a buzzword and more of a practical way to protect the system.

What Is Predictive Maintenance for Sewer Systems?

Predictive maintenance for sewer systems is the practice of using data to estimate where maintenance is needed before a blockage, overflow, structural failure, or treatment plant overload occurs. The work is still very physical. Crews still clean lines, inspect manholes, repair defects, and respond to wet weather. The difference is that the maintenance plan is driven by evidence instead of habit.

In a traditional model, a utility may clean the same segments every year because that is what the schedule says. In a predictive model, the utility asks a better question: which segments are showing signs of restriction, deterioration, abnormal wet weather response, or repeat risk?

That shift can change the economics of collection system management. If a line is clean and low risk, it may not need the same attention as a line with recurring blockage signals. If a manhole digital twin shows rapid degradation, it may deserve attention before a nearby asset that looks older on paper but performs better in the field.

Why Calendar-Based Sewer Maintenance Falls Short

Calendar-based maintenance is easy to manage, but it treats unlike assets as if they are the same. A pipe with little grease, low sediment, and stable flow conditions may be cleaned just as often as a pipe with chronic buildup. That can waste crew time while leaving higher risk locations under-monitored.

Reactive maintenance has the opposite problem. It waits until the system announces the problem through a backup, overflow, odor complaint, or emergency call. By then, the utility may be dealing with overtime, public complaints, compliance exposure, and a repair under pressure.

Predictive maintenance sits between those extremes. It does not assume every asset needs equal attention, and it does not wait for failure. It uses smart data to continuously refine where maintenance has the highest value.

What Smart Data Means in a Wastewater Collection System

Smart data is not just more data. It is data that helps a utility make a better decision. A dashboard full of unprioritized readings can be just as frustrating as a file cabinet full of old inspection reports.

For wastewater collection systems, useful data may include acoustic blockage scores, flow or level changes during rain events, manhole condition measurements, inspection history, cleaning history, known grease areas, pipe age, material, slope, customer complaints, and overflow history.

RH Borden’s Acoustic Assessments using the SL-RAT are designed to help utilities screen gravity sewer lines quickly, then focus cleaning and CCTV resources where the data shows a likely restriction. That is a practical example of smart data because the result can change what crews do next week.

How Predictive Maintenance Works in Practice

A predictive maintenance program usually follows a repeatable cycle. The utility collects field data, organizes it by asset, scores or ranks risk, dispatches work, then feeds the results back into the plan.

The cycle matters because predictive maintenance is not a one-time study. Sewer systems change. Grease patterns change. Rainfall patterns change. New development changes flows. Repairs remove some risks while other assets continue to age.

1. Collect condition and performance data

The first step is to collect data at a scale that supports decisions. That may mean acoustic screening across thousands of feet of pipe, level sensor data across a basin, manhole virtualization, or targeted CCTV where screening suggests a deeper look is needed.

2. Convert data into risk categories

Raw data becomes useful when it is converted into categories such as clear, monitor, clean, inspect, repair, or rehabilitate. A simple risk model is often better than an overly complex model nobody trusts.

3. Prioritize crew work

Once risk is ranked, crews can focus on the assets most likely to affect service, compliance, or cost. That is where predictive maintenance becomes operational rather than theoretical.

4. Update the model after work is completed

Every cleaning, inspection, and repair should improve the next round of planning. If the model says a line is restricted and cleaning confirms the finding, confidence increases. If the field result is different, the model can be adjusted.

Reactive vs. Preventive vs. Predictive Maintenance

Maintenance approach	How it works	Main advantage	Main limitation
Reactive maintenance	Respond after a blockage, overflow, or complaint	Simple to understand	Often expensive, disruptive, and urgent
Preventive maintenance	Clean or inspect assets on a fixed schedule	Creates routine and accountability	May over-maintain low risk assets and miss changing conditions
Predictive maintenance	Use condition and performance data to prioritize action	Targets work where risk is highest	Requires reliable data, process discipline, and field validation

Where Acoustic Assessments Fit

Acoustic assessments are especially useful because they can screen a large amount of pipe without entering the flow, sending a camera through every segment, or treating CCTV as the first step for everything. The result is a fast blockage assessment that helps decide where cleaning or CCTV is actually warranted.

That makes acoustic screening a strong front-end tool for predictive maintenance. It helps a utility separate likely clean lines from lines that deserve attention. Over time, repeated acoustic assessments can also show whether maintenance decisions are improving system condition.

RH Borden’s acoustic assessment model is built around helping collection system teams identify which pipe segments need cleaning, then reserve more expensive resources for the assets where they can produce the most value.

Where Digital Twins and Manhole Data Fit

Pipes are only part of the system. Manholes can degrade, leak, create access issues, and contribute to long-term risk. A predictive maintenance program that ignores manholes is missing a major part of the collection system picture.

Digital twins can help utilities evaluate manhole condition without relying only on static notes or occasional manual inspection. A virtual model can document dimensions, visible deterioration, and rehabilitation needs in a format that is easier to compare over time.

RH Borden’s Manhole Virtual Models use digital twin technology to create a virtual copy of each manhole, quantify degradation, and support safer asset management decisions. That type of information can help predict when maintenance or rehabilitation will be required.

How to Start a Predictive Maintenance Program

The best starting point is usually a pilot with a clear operational question. For example: which lines in this basin should be cleaned first? Which manholes show the worst degradation? Which part of the system is contributing the most I&I during rain events?

A focused question keeps the project grounded. It also makes it easier to show value to operators, managers, and elected officials who may be skeptical of another technology project.

Step 1: Choose a basin or asset class

Start where the stakes are clear. A basin with known I&I, repeat blockages, capacity constraints, or high cleaning cost gives the team a practical test case.

Step 2: Gather the data that can change decisions

Do not collect data just because it is available. Collect the data that helps answer the maintenance question. For some systems, that means acoustic pipe assessment. For others, it means high density sensor monitoring, manhole digital twins, or a combination.

Step 3: Build a simple priority list

The output should be understandable: clean these segments, inspect these segments, monitor these assets, rehabilitate these structures, or investigate this basin further.

Step 4: Validate with field results

Predictive maintenance earns trust when the field team sees that the data reflects reality. Cleaning results, CCTV findings, and repair notes should feed back into the next plan.

Questions to Ask Before Investing in Smart Sewer Data

What decision are we trying to improve?
Which assets create the most operational, financial, or compliance risk?
Do we need screening data, detailed inspection data, flow data, or manhole condition data?
Can the data be tied back to specific assets in our GIS or maintenance system?
Will field crews understand and trust the recommended actions?
How will we measure whether the program reduced unnecessary cleaning, emergency response, or repeat problems?
Who owns the process after the first dataset is delivered?

Turning Smart Data Into a Stronger, Longer-Lasting Sewer System

Predictive maintenance is not about replacing the crews who know a system best. It is about giving them better information so their time and budget go toward the assets that actually need it. When cleaning, inspection, and repair decisions are driven by field data instead of a fixed calendar, utilities catch problems earlier, avoid unnecessary work, and get more life out of aging infrastructure.

RH Borden and Company LLC, based in Lehi, Utah, helps municipalities across the country make that shift from traditional, labor-intensive maintenance to smarter, technology-enabled infrastructure management. Our Acoustic Assessments using SL-RAT technology quickly flag which pipe segments need cleaning first, our BASINiQ sensor networks pinpoint inflow and infiltration sources across a basin, and our Manhole Virtual Models create digital twins of critical assets to guide condition assessment and rehabilitation planning. Together, these tools help collection system teams reduce unnecessary maintenance, extend asset life, and build a maintenance plan grounded in what is actually happening underground.

Ready to see what predictive maintenance could look like for your system? Visit rhborden.com or call 385-510-3223 to get started.

Frequently Asked Questions

What is predictive maintenance for sewer systems?

Predictive maintenance uses condition and performance data to decide where sewer maintenance should happen before a failure, overflow, or emergency response is needed.

What data is used for sewer predictive maintenance?

Common data sources include acoustic assessment scores, I&I sensor readings, manhole digital twins, CCTV findings, cleaning history, work orders, complaints, and overflow records.

Is predictive maintenance the same as preventive maintenance?

No. Preventive maintenance usually follows a fixed schedule. Predictive maintenance adjusts the schedule based on actual asset condition, performance, and risk.

How do acoustic assessments support predictive sewer maintenance?

Acoustic assessments help identify likely blockages or restrictions quickly, so utilities can prioritize cleaning and CCTV instead of treating every pipe segment the same way.

Can smart data help with I&I problems?

Yes. Sensor networks and basin-level monitoring can show where wet weather flow is entering the system, which helps utilities focus inspection and repair work in the right area.

Do utilities need a full digital twin before starting?

No. A utility can start with a focused pilot using the data most relevant to one maintenance question, then expand as the process proves useful.

How does RH Borden help utilities use predictive maintenance?

RH Borden helps utilities collect practical sewer condition data through acoustic assessments, BASINiQ I&I location services, and manhole digital twin technology.

Ready to move from reactive maintenance to smarter sewer planning? Learn how RH Borden can help your utility use field data to prioritize the right work first.