Failure Reporting, Analysis, Corrective Action System

Copyright 2009 GPAllied©
Failure Reporting, Analysis,
Corrective Action System
Presented by: Ricky Smith, CMRP

What is FRACAS?
• A Failure Reporting, Analysis, and Corrective Action
System (FRACAS)
– Process by which failures can be reported in a timely manner
– Analyzed so that a Corrective Action System can be put in place
and eliminate or mitigate the recurrence of a failure.
• The goal of FRACAS
– Help an organization better understand failures
– The reporting required to identify failures
– Actions required to eliminate or mitigate failures

What is FRACAS?

Maintenance’s Objective
“Mitigate Failures with an effective Maintenance Strategy”
- Preventive Maintenance (quantitative inspection, restoration,
replace, replenish, etc.)
- Condition Monitoring (ID of a Failure early enough a failure
can be mitigated) also known as PdM
- Run-to-Failure

PF Curve
Priority 5 Priority 4
Priority2Priority1
Ultrasonic Energy
Detected
Vibration Analysis
Fault Detection Oil Analysis
Detected
Audible Noise
Hot to Touch
Mechanically
Loose
Ancillary
Damage
Failure
Initiated
Condition
PRECISION PREDICTIVE PREVENTIVE RUN TO FAILURE
Time
Catastrophic
Failure
P-F Curve
EquipmentCondition

Proactive Maintenance

FRACAS? Issues?
“A Proactive Reliability Process is a supply chain. If a step
in the process is skipped, or performed at a substandard
level, the process creates defects known as Failures.
The output of a healthy reliability process is optimal asset
reliability at optimal cost.”
― Ron Thomas, former Reliability Director at
Dofasco Steel, Hamilton, ON

Causes of Failures
• No repeatable procedures
• Not executing PM to specification
• Not performing Corrective Maintenance to specification
• Personnel not trained (skills, maintenance, reliability)
• Parts not stored to specifications
• Not using the right tools
• Best Lubrication Practices unknown
• RCA is not applied or not known
• No auditing of Maintenance Activity or Work

Known Best Practice Data
• 15% of Maintenance Work is Execution of PM
• 15% is the results of the PM Execution
PM / PdM is a controlled Experiment
• 15% of Maintenance is Execution of PdM
• 35% is the results of PdM
• Emergency Work – Less than 2%
• Planned Work – 90%
• Scheduled Compliance – 80% (by day by week)

Source: John Moubray, Nolan & Heap
Failure Patterns
Time Time
Age Related = 11% Random = 89%
Bathtub
Pattern A = 4%
Wear Out
Pattern B = 2%
Fatigue
Pattern C = 5%
Initial Break-in period
Pattern D = 7%
Random
Pattern E = 14%
Infant Mortality
Pattern F = 68%
“Why is infant mortality so high?”
Maybe because we do not apply the PF Curve Philosophy?

PF Curve with Priorities
Identify defect, Plan and Schedule Work Zone
100%ReactiveWork

Work Priority Distribution
Priority 1 and 2 Work is Reactive
Priority 3, 4, and 5 Work is Proactive
PM PdM CPM CPdM REQ

PF Curve and Managing Proactive Maintenance
Point P
Point F

Work Priority Distribution – a Few Simple Rules
EMERGENT work is anything
that is done as a Priority 1 or 2
CORRECTIVE work is
anything done as a result of
an inspection
CORRECTIVE work should
never be done as a P1 or P2
ROUTINE work is anything
done as a PRIORITY 3
CORRECTIVE work can be
done as Priority 3
CORRECTIVE work should be
done as a Priority 4 and 5
Most CORRECTIVE work
should be done as P4 or P5 if
you embrace P-F mentality

FRACAS Enables Success of an Organization

“It isn’t what you know that will kill you, it is
what you don’t know that will”

It is all about the Data

How do you know where are?
How do you know the Direction to reach you objective.?

Without good data we are lost!

“Bad Data” resulting in High Variation
- Variation in your Maintenance Process
- PM Program not effective
- Variation in PM Compliance
- PM program not focused on specific Failure Modes
- Repeatable Corrective Maintenance Procedures not
available or if available not used
- Storeroom is a deathtrap for parts, little or no PM
program on critical spares

Definition of Data Quality – ISO 14224
Must have Confidence in the collected Reliability and
Maintenance data, and hence any analysis, is strongly
dependent on the quality of the data collected. High-quality
data is characterized by:
a) completeness of data in relation to specification;
b) compliance with definitions of reliability parameters,
data types and formats;
c) accurate input, transfer, handling and storage of
data (manually or electronic);
d) sufficient population and adequate surveillance
period to give statistical confidence;
e) relevance to the data users need.

The International Standard for Failure Data

Failure Data – ISO 14224

ISO 14224 Maintenance Taxonomy Standard

ISO 14224 Maintenance Taxonomy Applied

Effects of Good Data Quality

KPI #1 – Asset Health Report
Asset Health Metric - The percent of assets with no identifiable “Defect”

KPI #2 – Mean Time Between Failure
• Mean Time Between Failures (MTBF) is the average length of
operating time between failures for an asset or component.
(Definition extracted from Published SMRP Metrics)
• MTBF is used primarily for repairable assets and components of
similar type. (Definition extracted from Published SMRP Metrics)

KPI #2 - MTBF Example / Reporting by Taxonomy
Equipment Taxonomy (ISO 14224)
Systematic classification of equipment into generic groups based on factors
possibly common to several of the items
Area Level MTBF Component Level MTBF

KPI #3 – MTBF / “Equipment Condition Report”

KPI #4 – “Equipment Condition Report / Equipment Detail”

KPI #5 – Route Compliance

Route Compliance Impact on Asset Health Report

KPI #6 – Monthly Maintenance Cost % of RAV
• Maintenance Cost
- Labor Cost
- Material Cost
- Contract Maintenance Cost
- Overtime Cost
• Correlate Cost to Other KPIs

Data Accuracy Requirements
• Process Map
• Roles and Responsibilities Identified

The 7 Steps to a Successful FRACAS

Step 1 – Determine your end goal.
• The beginning of every journey starts with a destination
• The beginning of the journey to an effective FRACAS is the same as
any other
• You must have an end goal in mind
• The goal of a FRACAS is not to gather data, but to eliminate failures
from the organization
• Knowing this helps ensure that every policy, procedure, and activity in
the system is goal oriented
• The roles, goals, and responsibilities of everyone involved with the
system can be focused toward that goal
• Having the goal in mind allows you to build the shared vision and
values that will make the system work successfully for you

Step 2 – Create the Data Collection Plan
1. Determine the measures you will use
– MTBF
Mean Time Between Failures (MTBF) is the average length of operating time
between failures for an asset or component. MTBF is used primarily for
repairable assets and components of similar type.
– MTTF
Mean Time to Failure (MTTF) is the average length of operating time to failure
of a non-repairable asset or component, i.e., light bulbs, rocket engines. A
related term, Mean Time Between Failures (MTBF), is the average length of
operating time between failures for an asset or component. Both terms are a
measure of asset reliability and are also known as Mean Life.

Continued
2. Determine what data needs to be collected to create the desired
measures

Continued
3. Determine how the data will be collected
- There are different types of data that need to be collected
- Determine how the data will be collected
- Failure data can be collected through the EAM/CMMS, automated process
data systems, or by using checklists
4. Determine how data will be analyzed
– The best bet is probably to start with Pareto
– Just make sure to remember that data analysis - where to apply methods
JDI, RCA and RCM
– Do not fall victim to analysis paralysis. Analytical reports are nice, but no
statistic ever solved a problem

Keep it simple in the beginning
3rd
2nd
1st
# of Failure by
area
# of Failures by
Equipment
# of Failure by
Component or Part

Examples – Dominant Failure Pattern
MTBF –
Object Type
(Electric Motor)
Cause Code
=
# LOL = 5%
# MAERR = 10%
# LOP = 20%
# OPER = 65%
Failure
Pattern
Cause Code
=
# LOL
# MAERR
#LOP
# OPER
New Failure Pattern

Example Continues
• % of Assets with No Identifiable Defect
• Failure Rate for Specific Components
Drive Belt-Broken-Ageing Failure Rate
0 602.49 1205 1807.5 2410 3012.5 3614.9 4217.4 4819.9 5422.4 6024.9
Time
0
0.00010696
0.00021393
0.00032089
0.00042785
0.00053481
0.00064178
0.00074874
0.0008557
0.00096266
0.0010696
FailureRate
Regionalised rate
Distribution rate
P0: 0%
B20: 3492
B15: 3208
B10: 2858
ε: 0.05664
ρ: 0.9781
γ: 0
β: 3.745
η: 5212
Median rank
2-parameter
Weibull

Step 3 – Determine Organizational Roles, Goals,
and Responsibilities (RACI)
• Who collects the data?
• Who analyzes the data?
• Who takes what action based on analysis results?

Develop Process Map / Maps

Develop a RACI Chart for each Process Map
Decisions/
Functions
Maint.
Manager
Maint.
Supervisor
Reliability
Engineer
Maint.
Planner
Maint.
Tech
Failure Data Entry I A C I R
Data Accuracy A R C C C
RCA – Invalid Data I A R C R
Analysis of Data A I R C C
Actions Identified A C R I C
Actions Taken A R C C I
FRACAS Activated A R C C I

Step 4 – Create the FRACAS Policies and
Procedures Manual
• Policies and Procedures Manual that will serve as the basis for
managing and administering the FRACAS system
• This is a tedious step, but should not be skipped
• This Manual will serve as the basis for developing:
– Initial FRACAS Training for all key personnel
– Ensuring that new employees understand and participate in
FRACAS effectively
– Best to develop a FRACAS Management Manual and a pocket
size book that is easy for people to carry around and refer to as
required

What should be in the Manual?
• Let’s develop this together to insure we both agree on the content
• The manual should have at the minimum
– Definition of FRACAS
– Benefits of FRACAS
– Managements’ Guidance
– Managements’ Expectations
– Roles and Responsibilities by position
– FRACAS reports
– Key data which must be input to generate these reports
– What these reports will do for the mine
– Training Outline for each person based on RACI Charts

Step 5 – Develop FRACAS Training Plan
• Each person in the organization will need to be trained according to
their level of participation in the FRACAS
• Use the Tasks Listed on all RACI Charts
Decision
Function
Maint.
Manager
Maint.
Supervisor
Reliability
Specialist
Maint.
Planner
Maint.
Tech
Failure Data Entry
Data Accuracy
RCA – Invalid Data
Analysis of Data
Actions Taken
FRACAS Activated

Training Plan Requirements
- Training Plan should consist of the following
 Position : Maintenance Technician
 Task: Close out a Work Order
 Condition: Given 5 Completed Emergency Work Orders
 Standard: Close out Work Orders to 100% Compliance
 Method of Training: Lecture, Web, Reading, etc.
 Method of Validation: Written Test, Web Test, Verbal Recall, etc.

Step 6 – Implement FRACAS
• Let’s turn it on
– Publish FRACAS Policies and Procedures Manual
– Train required personnel
– Hold required informational meetings
– Begin data collection on highest priority area
– Analyze data and report results on Public FRACAS Information
Board
– Create corrective actions based on results.
• Mitigation of Human Error
• Changes to the current maintenance strategy
• Changes to how production operates equipment
• Resign Equipment

Step 7 – Monitor, Show Success, and Adjust
• Monitor data quality and results
Decision
Function
Maint.
Manager
Maint.
Supervisor
Reliability
Specialist
Maint.
Planner
Maint.
Tech
Failure Data Entry I A C C R
Data Accuracy A R C C I
RCA – Invalid Data A C R I C
Analysis of Data A I R C I
Actions Taken A R C R I
FRACAS Activated A R R C I

Monitor and Adjust
• Good data is the backbone of good decision making
• It is important to monitor data quality
• Make adjustments to either the data collection plan or the training
program
• Insure data is consistent and informative
• Many organizations believe they have good data only to find out
their data collection is inconsistent

Questions?

Failure Reporting, Analysis, Corrective Action System

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Failure Reporting, Analysis, Corrective Action System