ReliabilityX360

ReliabilityX360 is an AI-powered decision-support solution for critical plant assets. It combines domain expertise, machine learning, asset-specific configuration, and historian and DCS data to identify developing equipment issues early and provide reliability teams with actionable insights before potential failures occur.

Rather than relying on alerts that trigger too late or too frequently, ReliabilityX360 identifies active failure modes, estimates time-to-failure, determines the parameters contributing to the issue, and recommends appropriate actions. Its configurable framework can be adapted to the asset’s own build, subsystems, sensor availability, and failure modes.

Salient Features

Early Deviation & Failure Detection

Use deep learning models to learn normal asset behavior, identify deviations early, and track how equipment performance moves away from design and rated operating conditions.

Failure Mode Identification & Diagnosis

Build an asset-specific failure mode matrix that correlates operating parameters with domain logic to identify active failure modes and rank the parameters contributing to them.

Time-to-Failure Estimation

Forecast critical parameters and estimate the remaining time before they cross defined failure thresholds, giving reliability teams an earlier window to plan intervention.

Asset & Subsystem Health Intelligence

Evaluate subsystem health, identify the subsystem limiting overall asset performance, and provide prescribed workflow actions for identified failure modes.

Customizable Limits, Priorities, etc.

a. Engineers can manipulate configured KPI limits based on plant priorities.

Sensitivity Analysis

Optimax provides feature to run multiple cases simultaneously with different limit ranges to enhance the actionable insights.

Role-Based Access and Security

Provides access control to ensure only authorized personnel can view or edit monitoring configurations.

Result Analysis Tool

Offers diagnostic tools to help engineers quickly investigate and resolve performance issues with different configurations

Client Benefits

30+ Days Advance Warning

Provide advance warning on alerts before failure, giving reliability teams additional time to investigate, plan, and take appropriate action.

$2M+/Year Savings Potential

Deliver potential savings of more than $2 million annually per critical asset protected by helping avoid unplanned trips and supporting controlled intervention.

<2% False Alert Rate

Reduce unnecessary alerts and provide reliability engineers with findings they can act on with greater confidence.

Improve Maintenance Planning

Combine failure-mode identification, parameter contribution, and time-to-failure estimates to help teams prioritize interventions based on developing asset conditions.

Dashboard & Snapshots

Configure all tags related to specific Equipment:

Solution configuration [Defining the Objective: Maximize Production, Minimize Utility]:

Solution configuration [Defining cases of interest – Multiple cases can also be added]:

Result Tag Configuration: Identify the potential actionable insights.

Sensitivity Analysis: Supporting engineers to evaluate appropriate configurations

Case Studies

Development of Soft Sensor to predict the C5 contents in Debutaniser column Overhead:

Background

In modern petrochemical operations, maintaining product specifications within tight limits is critical for both quality assurance and process efficiency. One of the key challenges faced by a leading chemical plant was the need for real-time prediction and monitoring of C5 content in the overhead stream of the debutanizer column — a crucial step to ensure product specification compliance and optimize operations.

Challenge

Traditional laboratory testing methods for measuring C5 content introduced significant time delays, making real-time adjustments difficult. The plant required a fast, accurate, and reliable solution to continuously monitor C5 levels, allowing operators to take timely corrective actions and avoid off-spec products.

Solution with AnalyticX

Using AnalyticX, the engineering team was able to develop a soft sensor — a machine learning-based predictive model — specifically designed to estimate the C5 content in the debutanizer overhead stream in real time.

Key steps included:

Seamless ingestion of historical operational data into AnalyticX without any coding requirements.
Applying statistical analysis to identify key process variables influencing C5 concentrations.
Rapid development and training of a machine learning model tailored to predict C5 content with high accuracy.
Deployment of the predictive model into live operations for real-time product spec monitoring.

Results

Enhanced Product Monitoring:
Operators now have a real-time view of the predicted C5 content, significantly reducing reliance on delayed lab measurements.
Improved Process Control:
By closely tracking C5 levels, the plant was able to make timely adjustments to operational parameters, ensuring consistent product quality.
Reduced Off-Spec Production:
Real-time insights helped minimize off-spec batches, leading to material savings and reduced reprocessing costs.

Key Takeaways

AnalyticX enabled the rapid development of a soft sensor customized for critical KPI monitoring.

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