Maximo Reporting

Identification of the underlying problem

Whenever a component or product fails in service or if failure occurs in manufacturing or during production processing, failure analysis plays a very important role. In any case, one must determine the cause of failure to prevent future occurrence, and/or to improve the performance of the device, component or structure.

Typical examples of systems/equipment that can be analyzed are electrical generators, heat exchangers, valves, control systems, pumps, components of gas turbines and compressors.

Failure Analysis will disclose;
• Why the event, failure or breakdown occurred
• How future failures can be controlled or eliminated

Analysis to Identify the Causes of Failure / Breakdown

Failure analyses of the repairable systems focus on the model capability to identify, control and eliminate future failures, for a system.
• Root Cause Analysis
• Pareto Analysis for Downtime
• MTBF-MTTR Trending for Bad Assets
• Effect of MTTR on Asset Availability
• Breakdown Analysis
• Identification of Dominant Failure Codes
• Effect of Unplanned Cost on Maintenance Cost
• Analyze Reactive Maintenance
Benefits
• Uses advanced investigative techniques
• Identifies early (unlikely) life failures
• Extends equipment lifetime
• Reduced cost of maintenance
• Improves availability “up-time” and increased production
• Increases safety
• Easy to identify for potential losses where risk is included

For successful “Asset Maintenance & Reliability Management”, companies not only adopt many methodologies like RCM, TPM, CBM, etc. but they also leverage their asset related data to improve processes & performance. To make data driven informed decisions, companies require much more than just investment in the technology infrastructure they also require quality data consistently for trustworthy analysis. There are many challenges in achieving this...

Quality Data Preparation

• Multiple data sources with voluminous data
• Identification of good & bad data
• Need for reliable analysis
• Lack of data uniformity
• Data in non- reportable format
• Cleansing data manually
• Laborious quality data preparation
• Complex data validation

Advance Data Analytics

• Don’t know how to use data Can't leverage on data for analysis
• Asset health assessment
• Less asset availability
• Increased unplanned breakdown
• Lack of domain expertise & experience to analyze
• Unable to analyze & predict events critical to business operation

Managing Analytics Infrastructure

• Lack skilled & trained professionals
• Hiring full time resources
• Huge capital investment
• Lack expertise & experience
• Developing libraries from scratch
• Losing focus from core business functions
• Cost control

Asset Data Quality

• Asset data audit & quality Analysis
• Asset master data development
• Asset data dictionary development
• Asset data cleansing & validation
• Ongoing support

Data Analysis

• Problem Definition
• Data Availability Analysis vis-à-vis Analytics Feasibility Study
• Solution Modeling Using
• Solution Implementation
• Data gathering,
• Report development
• QA
• Result Analysis & Interpretation
• Report Preparation & Delivery
• Ongoing Model Effectiveness & Performance Monitoring


Statistical Modeling Techniques

• Weibull
• Monte Carlo
• Crow-AMSAA
• Pareto
• Croston
• Poisons
• Log Normal
• ARMA
• Exponential Smoothing
• ANOVA
• IR/Control Charts

Business Issues

• Identify underperforming Assets
• Identify equipment to be replaced
• Avoid unnecessary PMs
• Establish PM & failure relationship
• Identify unusual PM costs
• Monitor impact of strategy & process changes
• Determine future Maintenance cost for budgeting
• Finding equipment which have unusual # of failures
• Identifying most impactful failure causes on reliability
• Find optimum spare parts level

Ready to use models for

• Optimum PM Interval
• EOQ Calculator
• Forecast residual life of asset
• Predict TBF & MTTR
• Mean Residual Life of equipment
• Reliability Growth Models & Plots
• Repair Vs Replace Model
• Root Cause Analysis
• Spare Parts estimation Using Crow
• AMSAA Model
• ABC Analysis
• Spare Parts Demand Estimation
• Proportional Hazard Model
• Failure data Analysis
• Competing risks using survival analysis