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

Priority 2 Priority 1

Ultrasonic Energy Detected

Vibration Analysis Fault Detection Oil Analysis

Detected

Audible Noise

Hot to Touch

Mechanically Loose

Ancillary Damage

Failure Initiated

Cond

ition

PRECISION PREDICTIVE PREVENTIVE RUN TO FAILURE

Time

Catastrophic Failure

P-F Curve Eq

uipm

ent C

ondi

tion


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)

Copyright 2009 GPAllied© 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% R

eactive Work


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

Failu

re Ra

te

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-parameterWeibull


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

Technology

maintenance work

corrective work isanything

planned work

p2routine work

gpalliedproactive maintenance

data types

completeness of data

data users