Dexcent IDS

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Condition Based Maintenance Readiness Assessment

Condition-based maintenance readiness assessment enable organizations to evaluate their readiness for implementing advanced maintenance strategies that leverage real-time asset condition data. By conducting these assessments, organizations can identify gaps, plan effectively, and successfully transition to more proactive and efficient maintenance practices, leading to improved asset performance and reduced operational disruptions.

Technology Evaluation : Assessing the current technological capabilities and infrastructure needed to support condition-based monitoring. This involves evaluating the availability of sensors, monitoring systems, data collection tools, and the integration of these technologies with existing asset management systems.

Data Collection and Analysis Readiness : Assessing the organization's ability to collect, manage, and analyze data from various sensors and monitoring systems. This includes evaluating data quality, accuracy, and the organization's capacity to derive actionable insights from the collected data.

Asset Prioritization and Criticality Assessment : Identifying critical assets and prioritizing them for condition-based monitoring. Assessing which assets are most crucial to the organization's operations and determining the best CBM strategies for these assets.

Skillset and Training Readiness : Assessing the skillsets of maintenance and operational staff required to effectively implement condition-based maintenance. Providing necessary training to personnel on using CBM technologies, interpreting data, and performing maintenance tasks based on condition assessments.

Risk Assessment and Mitigation : Evaluating risks associated with adopting condition-based maintenance, including technological risks, potential data inaccuracies, and risks related to asset performance. Developing strategies to mitigate these risks to ensure smooth implementation.

Performance Metrics : Establishing performance metrics and key performance indicators (KPIs) aligned with condition-based maintenance strategies. These metrics help in measuring the effectiveness of CBM, such as asset uptime, mean time between failures, or cost savings due to reduced maintenance.

Change Management and Corporate Culture Readiness : Assessing the organization's readiness for cultural and process changes associated with adopting CBM. Ensuring that there is buy-in from stakeholders and a culture that embraces data-driven maintenance strategies.


Enhanced Equipment Reliability : Condition-based maintenance enable organizations to monitor equipment health in real-time using sensors and data analytics. This proactive approach helps in predicting equipment failures before they occur, ensuring higher reliability and reduced downtime.

Optimized Maintenance Schedules : By leveraging data from condition monitoring, organizations can optimize maintenance schedules. Maintenance activities are performed when needed based on the actual condition of the equipment, reducing unnecessary maintenance and maximizing uptime.

Cost Savings : Condition-based maintenance assessments help in cost reduction by minimizing unplanned downtime and preventing catastrophic equipment failures. This leads to lower maintenance costs, reduced emergency repairs, and optimized use of maintenance resources.

Improved Asset Performance: Assessments facilitate better asset performance by identifying patterns and trends in equipment behaviour. This data-driven approach allows for proactive decision-making, enabling organizations to optimize asset performance and extend asset lifespan.

Increased Safety and Compliance : Real-time monitoring and predictive analytics improve safety by identifying potential safety risks in equipment and addressing them proactively. Additionally, adherence to maintenance schedules ensures compliance with safety and regulatory standards.

Data Driven Insight in Decision Making : Condition-based maintenance readiness assessments generate valuable data insights. These insights aid in making informed decisions about equipment maintenance, replacement, or upgrades, optimizing asset utilization and driving strategic planning.

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Table of Content

1. Purpose
1.1. Purpose and Goals
1.2. Why The Industrial DataOps Process Is Needed?
1.3. Industrial DataOps Practitioner Engagement
1.3.1. Oversee An Existing Industrial DataOps Program
1.3.2. High Data Secrecy Organizations
1.3.3. Full Engagement
1.4. Principles
1.4.1. Know Your Data
1.4.2. Curate Your Data
1.4.3. Unify Your Data
1.4.4. Analyze Your Data
1.4.5. Hardware, Software, and People Working Together
1.5. Lifecycle
2. Intention
2.1. Scope
2.2. Assumptions
3. Terminology & References
3.1. Definitions
3.2. Acronyms and Abbreviations
3.3. Industry References, Standards, Regulations and Guidelines
3.4. Site Related References, Standards, Regulations and Guidelines
4. Expectations and Responsibilities
4.1. Roles
4.2. Role Job Description
4.3. Role Assignment
5. Opportunity Identification
5.1. Need Initiated
5.2. Improvement Initiated
7. Baselining
7.1. Data Rationalization
7.2. Data Justification
7.3. Data Impact
7.4. Data Flow
7.4.1. Data Producer
7.4.2. Data Path
7.4.3. Data Consumer
7.5. Data Good State
7.5.1. Failure Conditions
7.5.2. Warning Conditions
7.5.3. Abnormal Conditions
7.6. Data Processing Team
8. Target Confidence Factors
9. Critical Success Factors
10. Risk Analysis / Mitigation Plan
10.1. Risk Analysis
10.2. Mitigation Plan
11. Technology Selection
11.1. Hardware
11.2. Software
11.3. People
12. Project Execution
12.1. Project Synergy
12.2. Project Synergy
12.3. Resource Acquisition
12.4. Scheduling
12.5. Implementation
12.6. Training
12.7. Maintenance
12.8. Contingency
13. Evaluation Vs Baseline
14. Calibration & Sustainment
14.1. Training
14.2. Maintenance
14.3. Obsolescence
15. Continuous Improvement Process
15.1. Continuous Process Documentation
15.2. Audit
16. Management Of Change (MOC)
16.1. Applicability
16.2. Methodology