PdM
Predictive
Maintenance

Predictive maintenance is a term widely used by reliability, maintenance and operation groups. Shape’s proposal for predictive models considers the development of artificial intelligence tools, ingesting signals generated by field sensors.

SHAPE’S CONDITION
MANAGEMENT

Integrate available
data into a single
source of truth

Transform data
into insights
that address
business needs

Close the gap
between digital
solutions and
front liners

From the identification of patterns and their correlation with past events of anomalies and failures, predictive models make inferences about the health status of monitored equipment. The PdM module has an overview of the plant on a heat map, monitoring both asset health and signal quality. This visualization filters signals so that engineers and operators direct attention to equipment and failure modes that effectively requires.

The alarm center gathers all notifications of anomalies generated by predictive models, in cockpits optimized for efficient decision making of reliability, maintenance and operation engineering groups. On this same screen, the user can feed back the machine learning models so that they always remain at the best possible performance.

The alarm center concentrates:

Active alarms that require attention from reliability,
maintenance and operation teams;

Alarms that are in the process of analysis regarding the identification of root cause and actions to be taken;

Alarms already closed, which make up a base of lessons learned. The alarm center gathers all notifications of anomalies generated by predictive models, in cockpits optimized for efficient decision making of reliability, maintenance and operation engineering groups. On this
same screen, the user can feed back the machine learning models so that they always remain at the best possible performance

SHAPE’S TAKE ON PdM EVOLUTION

Predictive Maintenance (PdM) models aim to detect issues early, when they are still minor. Offering a large reaction time and minimal wear to equipment.

PdM 1.0 | Condition-based maintenance

PdM 2.0 | Equation-based predictions

PdM 3.0 | Fit-for-purpose analytics suíte

PdM 4.0 | Asset-wide analytics system

MACHINE LEARNING AND AI

The model is trained with a supervised approach over a period, it learns what is pre-failure behavior and what is not.

It is then tested – blind test – in a different period, alarming the failure
events of that period.

OBJECTIVE

Anticipate failure events, to give enough time for the maintenance
teams to act and prevent downtime.

HANDS ON THE PRODUCT

The possible interface of the Condition Monitoring dashboard at the
equipment level are all included in the PdM solution.

Heatmap

The heatmap screen provide managers a bird’s-eye view of the current
condition of multiple plants, helping them allocate resources to diagnosis and treatment.

Alarm Manager

The Alarm manager enables users to quickly identify equipment with
high-risk failure mode and act to prevent it. Notifications are sent to
stakeholders to ensure action in time.

Asset Dashboard

The asset-level dashboard is the one-stop shop for all information
about each asset, including the alarms generated by all predictive and
simulation models.

Performance

Integrated visualization of value captured by the solutions in downtime
avoided historically. Includes asset performance benchmarking and
failure events breakdown.

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