1. Risk Based Predictive Maintenance Solutions for Cleaning Sewer Mains

2. 01 05 02 06 03 07 04 Background and Goals Visualization and Grouping
Cleaning Philosophies
Cleaning Optimization Tools 03 07
Cleaning Frequency Optimization
Results 04
Code-based Data Collection

3. 01
Background and Goals

4. CCU System Background
Winston Salem Forsyth County – City/County Utilities Commission
1,750 miles of sewer
50 lift stations
2 WWTPs

5. Winston Salem Program Goals
Reduce SSOs
Reportable and Non-Reportable
Use Limited Resource Efficiently
Develop a Risk Based Schedule
Clean the right pipe at the right time
Clean the Entire System
Move from reactive to proactive
Leverage Data Collected

6. Historical SSOs Total SSOs SSOs by Cause State Reportable
Non-Reportable
SSOs by Cause
High Percentage of O&M caused SSOs

7. Data Review Pull Available Data to Develop a Risk Based Schedule SSOs
Cleaning History
Service Requests
CCTV
Annual Cleaning “Heat” Maps
Contractor Work
Cleaning
Root Control

8. Cleaning Philosophies02
Cleaning Philosophies

9. Cleaning Performance Indicators
Production
Footage cleaned
Set-ups performed
Effective
SSO reduction
Cleaning QC (pass rate)
Efficient
Cleaning at the right time
Crew A
Crew B
Crew C

10. Route-based Cleaning

11. Asset-based Cleaning

12. Route vs Asset Based Route Cleaning Asset Cleaning Pro’s Con’s
Productivity Based
Cleaning pipe based on need
Clean more footage
Better use of limited resources
Less windshield time
Not “over-cleaning”
Limited data needs
Con’s
“Over-cleaning”
Potentially less productive
Damaging Pipes
More windshield time
Not an effective use of resources
DATA NEEDS

13. Cleaning Schedule Optimization
Strategic Goal: Reduce SSOs
Primary Cause: Maintenance Related
Tactic: Use data to maintain assets at “right time”:
Benefits:
Focus limited resources where they are needed most
Reduce LOE to make decisions
Utilize data that already exists or is being collected
Extend useful lifetimes of pipeline assets

14. Schedule Optimization
Each pipe has an optimum cleaning frequency
Too Little Just Right Too Much
Efficient use of resources
Limit risk of overflow
Extend useful life
SAVE MONEY
Risk of overflow
Inefficient Resource Use
Increase wear

15. Keys to SSO Reduction VIA Sewer Cleaning
Office
Frequency optimization process
Optimized Work Order creation and assignment process
Field
Quality Equipment
SOPs – techniques and data collection
Training – techniques and data collection
QA/QC Program – techniques and data collection
Both
Balance between quantity and quality of cleaning

16. Asset Based Cleaning Tools Needed
Measure the maintenance condition of a pipe
Determine asset cleaning frequency
Group assets onto work orders in geographic clusters

17. Code-based Data Collection03
Code-based Data Collection

18. Code-Based Cleaning Data
Findings
Severity
Grease
Clear
Roots
Light
Debris
Medium
Heavy

19. One-to-One Work Orders
Before
After
Steve – First thing we had to change was the way work orders were generated/completed. Need to collect code based findings on every asset rather than entire work order. Data isn’t very accurate if the same findings are used for a group of pipes. One may have Heavy Grease and the other 5 pipes on the work order may be clear. Existing system would assume all 6 pipes have Heavy Grease.

20. Cleaning Frequency Optimization04
Cleaning Frequency Optimization

21. Cleaning Optimization Premise
Based on two concepts:
Data driven decisions
Pipes have cleaning “windows”
Data
System Recommendation
User
Decision

22. CLEAR LIGHT MEDIUM HEAVY DECREASE FREQUENCY FREQUENCY IS RIGHT
Data Driven Decisions
CLEAR
DECREASE FREQUENCY
Any bullets here?
LIGHT
FREQUENCY IS RIGHT
MEDIUM
INCREASE FREQUENCY
HEAVY
INCREASE FREQUENCY

23. Cleaning Schedule Windows
Target
Complete
Last Completed Date
Next Date
Target Start Date
Date
1 Month
6 Month
12 Month
60 Month
Time
Time

24. Visualization and Grouping Tool05
Visualization and Grouping Tool

25. Visualize Pipes to Schedule
Benefit: Allow the User to quickly and effectively balance risk and geographic location when assigning work
Facilitates balance between WO risk and location

26. Cleaning Optimization Tools (COTools)06
Cleaning Optimization Tools (COTools)

27. Cleaning Optimization Tools Setup
Target
Complete

28. COTools User Interface

29. Steve – Now it is all in the new Cityworks system
Steve – Now it is all in the new Cityworks system. Can create a single work order with many pipes and get findings for each pipe. The planner scheduler uses this GIS view to select groups of pipes that need to be cleaned. Planner scheduler looks for red pipes first and any other colors surrounding it to increase efficiency and limit windshield time.

30. 07
Results

31. SSOs

32. HDRs Practical Approach has Led to Successful Programs

33. Questions? kimd@cityofws.org O: 336.771.5106 M: 336.462.1765 O: M: