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features
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Description

Enhancing reliability, performance, and safety stands as a primary focus for industrial enterprises and businesses in the current landscape. Organizations are channeling their resources and efforts into managing expenses effectively while striving to maximize value from their existing assets. AVEVA Predictive Analytics empowers these organizations to achieve optimal returns on essential assets by facilitating predictive maintenance (PdM) initiatives. The AVEVA Predictive Analytics solution offers advanced early warning notifications and diagnostics for equipment problems, potentially identifying issues days, weeks, or even months before they lead to failures. This proactive approach allows asset-heavy organizations to minimize equipment downtime, boost reliability, and enhance performance, all while decreasing operational and maintenance costs. Furthermore, AVEVA Predictive Analytics seamlessly integrates with various control and monitoring systems and supports deployment either on-premises or in a cloud environment. Its highly scalable architecture enables the monitoring of anything from a single asset to entire plants or even hundreds of remote assets spread across multiple locations, ensuring comprehensive oversight and management capabilities. Ultimately, this solution aids organizations in navigating the complexities of asset management in an increasingly competitive market.

Description

SmartCBM, developed by Allied Reliability, serves as a holistic condition-based maintenance solution aimed at identifying potential component failures that can result in unexpected operational halts or excessive costs. By leveraging advanced predictive maintenance technologies, along with process data, statistical process control, and data analytics, SmartCBM facilitates the design, implementation, execution, and optimization of maintenance strategies, ultimately enhancing the uptime and dependability of your machinery. The platform accommodates a variety of data collection techniques, such as manual route-based, sensor-based, inline, and wireless periodic monitoring, thus providing a comprehensive and economically viable approach tailored to the specific needs of each asset. Moreover, SmartCBM includes an array of proprietary tools for thorough monitoring and reporting of asset conditions, which aids in formulating maintenance strategies that address design-failure modes, thereby improving equipment reliability and availability. Its versatility and effectiveness make it an indispensable tool for organizations seeking to enhance their maintenance processes and reduce operational risks.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

$5,000 per year
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows No 
Mac Yes 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Aveva

Founded

1967

Country

United Kingdom

Website

www.aveva.com/en/products/predictive-analytics/

Vendor Details

Company Name

Allied Reliability

Founded

1997

Country

United States

Website

www.alliedreliability.com/smartcbm-condition-monitoring

Product Features

Predictive Analytics

AI / Machine Learning No 
Benchmarking No 
Data Blending No 
Data Mining No 
Demand Forecasting No 
For Education No 
For Healthcare No 
Modeling & Simulation No 
Sentiment Analysis No 

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