NEM Solutions Wheelset Life Extension and Asset Monitoring Software for Rail Operations
NEM Solutions offers expert solutions and services for railway wheel life extension, productivity improvement, condition monitoring and business-driven data management.
NEM has developed a technology named A.U.R.A, which extracts data from monitored assets to provide highly automated diagnostics and fleet condition management.
A.U.R.A brings all data and information together, determines the health of assets, predicts future conditions and notifies of potential asset failures. It delivers optimisation of operations and maintenance activities.
Extending the life of wheelsets and bogies in railways and rolling stock
NEM provides solutions and services to maximise lifecycles, while increasing productivity and availability. Through wheel condition monitoring, A.U.R.A Wheel provides full management of the long-term planning of railway bogie and wheelset maintenance, reducing uncertainty in the planning of maintenance activities throughout the lifetime of wheelsets in the rolling stock fleet.
The availability of ‘big data’ techniques provides NEM Solutions with the ability to extend wheel lifecycles in accordance with customer needs. This leads to more efficient and effective maintenance, as well as increased fleet availability for service operations.
A.U.R.A wheel currently manages more than 26 million wheel measurements, for more than 40,000 vehicles, integrating information from wheel parameter acquisition devices, wheel surface defects monitoring devices, as well as lathes and third-party management solutions.
Alert and warning systems for consistency in operations and maintenance
NEM’s main objectives are to eliminate uncertainties for operations and maintenance (O&M) productivity improvements, and to anticipate and avoid failure through knowledge extraction and behavior prediction.
The principal process of A.U.R.A is to create behavioural patterns of asset normality using historical data and the automated generation of behavioural models. Deviations from normality are then monitored and processed in real-time, with warnings and alerts provided.
Diagnostic methods used include analysis of vibrations, temperatures, pressures, oil sample analysis, noise, electrical parameters, intensities, and other measurable physical attributes. In the absence of acquisition systems, NEM Solutions can assist in system selection and the pre-processing of data.
Condition monitoring systems for railways
NEM Solution’s focus on productivity improvement, products include:
- A.U.R.A Diagnostic: A predictive system for condition-based maintenance planning based on the early-detection and warning of potential equipment failures
- A.U.R.A Lube: Effective implementation and management of lubricated equipment condition monitoring, facilitating the process of oil sampling and subsequent analysis and diagnostics
- A.U.R.A Logs: A fully configurable management system that acquires, manages and classifies data alerts from subsystems across multiple fleets
Business-driven data management solutions
In-depth knowledge of business and industry is required to understand the management of data and information with regards to a company’s core business purpose. NEM Solutions has the capacity to offer total control of business, based on complex assets and combining in-house expertise in the railway industry with big data techniques and business intelligence.
Products related to NEM Solution’s business management capabilities include:
- A.U.R.A Maint: A flexible computerised maintenance management system (CMMS) specifically designed for the railway industry, where the key information is properly handled to generate valuable knowledge and to make decisions aimed at optimising your business
- A.U.R.A Desk: One single place for interacting with all the key information from A.U.R.A diagnostic, CMMS, power generation data, data acquisition systems, enterprise resource planning software (ERPs), and any other data source needed for business-driven data management
Products and Services
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