Peaxy Lifecycle
Intelligence

Predictive asset management for industrial equipment. Turns operational data into insights that optimize productivity, improving the lifetime value of assets. Enables new uptime-centric servicing opportunities.

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Peaxy Lifecycle Intelligence is a modular, scalable, cloud-based asset management application aligned with the needs of the value-driven enterprise. By rapidly turning operating data into financial insights, PLI lets operators minimize O&M costs and optimize performance, improving the lifetime value of industrial assets.

PLI is extensible, with modules that extend the core functionality in specific domains: Anomaly Detection adds predictive analytics to the alerting system; Digital Dossier manages the digital footprint of serialized equipment; other modules enable condition-based servicing and compliance regimes.

PLI serves a wide range of use cases involving precision-engineered equipment, including gas and steam turbines, wind turbines, compressors and propulsion installations.

Features

Asset tracking

Register and track industrial assets at the portfolio-, site- and unit level.

Real-time telemetry

Securely collect, store and display real-time sensor data from your assets.

Reporting and visualization

Gain insights from both live and historical data, at every level of hierarchy.

Role-based user management

User-centric functions and views for multiple roles — from field technician to CEO.

Alert notifications

User-defined notifications delivered across multiple channels — email, sms, web.

Anomaly Detection

Anomaly Detection

Trains a basket of machine-learning algorithms on historical failure data to discover non-trivial discrepancies in live data streams that can signal incipient component failures. These anomalies are fed into PLI’s alert management system, where the user can fully analyze the data.

By choosing to pre-emptively repair or replace equipment, operators can avert catastrophic failures. Because each asset class has a unique use case, Peaxy’s analytics experts perform the initial tuning and tweaking of algorithms. The module’s accuracy improves over time through continuous data ingestion, the use of multiple competing algorithms, and manual feedback on false-positive alerts.

Digital Dossier

Collects, analyzes and manages the digital footprint of each serialized physical asset under management. Both structured datasets and unstructured files are indexed, threading together a serial number’s engineering drawings, service manuals, service records, test and simulation data, and continuously updated live data.

By linking to ERP and PLM datasets, Digital Dossier can chart the relationships between serial numbers, enabling complex queries that make this information universally accessible across the enterprise. Digital dossier can also speed up common O&M tasks through automation — including integrity management, permitting, deployment, proactive maintenance, regulatory compliance, insurance and root cause analysis.

Service Manager

Provides maintenance planning tools that minimize O&M costs: A task manager turns alerts into action items, notifying assigned crews via a smart scheduler. Production losses are minimized by planning downtime around demand peaks and weather, and by grouping related tasks. Connectors integrate with the operator’s existing external tools, such as ERP inventory management systems or legacy asset management applications, like Maximo.

Ambient Profiler

Applies real-time location-based ambient data to performance- and stress models of deployed industrial equipment to better inform decision-making around extending operational windows. Enables a compliance regime with optimized maintenance and inspection plans, and calculates the resulting O&M savings. Provides payback analysis when negotiating sales and service agreements.

Advanced Reporter

Generates interactive reports for data sets using a toolbox of visualizations, such as control charts, scatterplots, chord diagrams, treemaps and network graphs. Reports can be preconfigured to define input criteria, output formats, trigger frequency and recipients. Outputs can visualize individual components, or else show the comparative performance of an entire class of components meeting specific criteria.

Use cases

Digital Dossier brings discipline to battery R&D

For manufacturers of industrial batteries, improving the storage efficiency, asset life and manufacturing yields of batteries is essential to maintaining a competitive advantage in this fast-moving technology sector. Testing new battery builds and electrolytes lies at the core of the R&D process, but if the experiments don't accurately define and document the building and testing of batteries, battery performance cannot reliably be traced back to correlating factors in the battery design or assembly.

Peaxy Lifecycle Intelligence (PLI) with Digital Dossier enables a fully digitized R&D process that enforces the discipline necessary to properly advance the science and lock down the tolerances needed in the manufacturing process. Where previously travelers were paper-based, data is now captured automatically by PLI or input directly by the assembly line operator.

The digital traveler extends through the entire lifecycle of the test battery: By capturing and analyzing all the test data, as well as the autopsies that follow it, every possible performance factor is parametrized and available to the experiment designers as a queryable feature.

Anomaly Detection helps wind farm operators predict turbine failures.

Wind farm asset management tools have long given operators a view into the live operating conditions of their assets, with rule-based status alerts pinpointing problems as they unfold, forcing a costly repair by field technicians. Proactively maintaining equipment health before problems occur would greatly reduce operating costs and downtime, so operators have long sought the holy grail of a reliable predictive maintenance regime.

Now, Peaxy Lifecycle Intelligence (PLI) machine learning algorithms can correlate specific patterns in wind turbine operational data with potential component failures in the weeks and months following. After PLI alerts wind farm operators to such incipient failures, the component in question can be repaired cost-efficiently, before a potentially catastrophic failure occurs.

PLI’s anomaly detection module works by training a number of distinct machine learning algorithms — both supervised and unsupervised — and selecting the most accurate ones. Training data must include at least a year’s worth of historical data, including failure data.

Digital Dossier and Anomaly Detection help rig operators find the root cause of non-productive time.

For drilling rigs, non-productive time due to unplanned outages has a significant impact on capital- and operating costs. Downtime for critical equipment like top drives, drawworks and circulation pumps can bring the entire rig to a standstill.

To reduce the likelihood of such outages, rig operators can implement a predictive maintenance regime, so that field technicians can proactively address operational issues before they generate failures. PLI’s Anomaly Detection module delivers alerts based on such predictive analytics.

To perform root cause analysis on such failures, the operator needs to collect and manage the digital footprint of every serialized drilling asset in its fleet, so that engineers have all the information and tools at hand to find a causal relationship. PLI’s Digital Dossier module connects an asset’s serial number to all related files and datasets, as well as to related serial numbers, so that the operator can build complex compound queries that identify and group similar cases.

Anomaly Detection helps reduce time taken to decide on corrective action for end-of-line test failures.

Manufacturers submit each drivetrain and turbocharger on the assembly line to an end-of-line test to ensure the component operates to specifications. For components that fail the test, a decision is needed: to repair and retest, or to scrap. The time required to decide on this next step contributes to lost productivity, and the financial impact can be precisely calculated.

To minimize this non-productive time, and to improve the accuracy of a required repair, manufacturers can implement a system that correlates test results with outcomes. For failing assemblies, the system needs to track whether the component is repaired or scrapped. If repaired, the system needs to record the type of repair performed and whether the retest is successful.

PLI with Anomaly Detection uses trained machine learning algorithms to reduce the time needed to decide on this next step, while also improving the accuracy of that decision. Digital Dossier tracks all assemblies by serial number, ensuring that test results, eventual repairs or reworks, and any subsequent retest results are appended to the serial number’s digital dossier.