TurbinePhD - Condition Monitoring System
From Condition Monitoring Systems
TurbinePhD is the most cost-effective and easy-to-use vibration-based condition monitoring system in the wind industry. This complete condition monitoring solution includes purpose-built hardware, automated analysis of the vibration data, and an intuitive web-hosted user interface. Unlike traditional condition monitoring systems, TurbinePhD was designed to be accessible to all levels of your organization, from site operations to asset managers—not just a tool for vibration experts.
Purpose-Built Solution
Purpose-Built Solution
We designed TurbinePhD to optimize the performance of the entire system while ensuring easy installation, worry-free operation, and minimal maintenance requirements. The primary building-block of the system is a networked smart sensor that integrates the accelerometer, data acquisition, vibration processing, and digital communication into one package.
Smart sensors have several advantages:
- Clean and easy installation
Digital communication (RS-485) supports a bussed architecture. Only one central cable is needed for communication with all sensors, simplifying installation. - Reduced electrical noise
Designing the data acquisition into the sensor eliminates the need for long cable runs back to a central data acquisition unit. This reduces electromagnetic interference (EMI), which can add noise to the vibration signal and make accurate diagnosis more difficult. - Excellent low-frequency response
The smart sensors use a MEMS accelerometer with excellent low-frequency response, important for the slow rotational speed of the wind turbine, and no calibration of the accelerometer is required. - Reliable operation
Each smart sensor runs a Built-In Test (BIT) to ensure proper function and eliminate false alarms due to faulty sensors. - Reduced network load
On-board vibration processing reduces the amount of data that needs to be sent to the server, reducing the data transmission requirement on the wind farm local area network.
Each installed TurbinePhD system communicates with a central cloud server where all the data is stored and users have easy access. The data is transmitted using the local area network of the wind farm, and in many cases, no additional network hardware is required. RNRG has created secure solutions for transmitting the data from the wind farm operational network to the cloud server.
Utilizing a cloud solution has several benefits:
- Reduced IT cost
There is no need for a local server to store the data, which reduces the initial cost of implementation and eliminates the hidden costs of server maintenance. - Data access anywhere
The user interface is web-hosted, avoiding the need to maintain expensive software packages for individual users and providing access to your turbine health data wherever you have internet service. - Secure remote updates
All software and firmware updates are made remotely without the need to VPN into the wind farm, increasing security.
Automated Diagnosis
With automated diagnosis, the current health of your turbines is accessible to all levels of your organization (including site technicians, site leadership, remote operators, and engineers), not just analysts who can interpret vibration data. This increases the organizational buy-in for your predictive maintenance program, allowing those closest to the turbines to monitor their health.
TurbinePhD’s automated diagnosis is based on advanced vibration processing techniques adapted from the rotorcraft industry. Each processing technique is designed to identify a particular fault mode of each component. For bearings, these fault modes includes macropitting, cracks, micropitting, and fluting. For gears, the fault modes include cracked/chipped teeth and gear face defects such as pitting. For shafts and couplings, the fault modes include imbalance, cracks, and misalignment.
Instead of relying on broadband alarms and highly skilled analysts to interpret vibration spectrum, the system automatically detects and diagnoses faults using site-tailored component thresholds. This minimizes false alarms and eliminiates the need to train and maintain an internal monitoring team or pay a third party to do it for you. If an alarm limit is exceeded, an automated e-mail alert is sent to let users know that a fault has emerged. This allows you to focus on the turbines that need attention, not on interpreting data.
Intuitive User Interface
While timely alerting of turbine faults is critical for a condition monitoring system, equally important is the ability to get to the critical information that you need efficiently so you can determine what the next steps should be. Our web-based user interface provides access to all of your turbine health data anywhere you have an internet connection.
With TurbinePhD, you can quickly and easily identify faulted turbines. All component health indicators are normalized to a common scale, from 0 to 1, so regardless of the component type or vibration processing methods used, all users can understand which components are faulted and how severe the fault is. Traffic light indicators of turbine and component health make interpretation easy, allowing users to quickly identify which turbines require attention. Users can quickly drill-down to the data from an individual component to verify the fault and determine the best course of action.
To create a predictive maintenance culture, it is also important to communicate the health of the turbines across your organization. TurbinePhD has built-in functionality that allows users to quickly create reports that convey the critical information about the health of their turbines. With the alarm management system, you can track which alerts require attention and which have been acknowledged
Cost-Effective Design
Purchasing a CMS is one of the largest investments a wind operator makes. Finding the capital budget to reduce your long-term operations expenses can be difficult, so RNRG designed a system that is cost-effective while providing the diagnostic coverage operators need.
- Scalable system
The system is completely scalable so you can choose to monitor only the components that you find valuable without paying for extra hardware. A user could choose to monitor only the high speed components (HSS and generator) that can be repaired up-tower to increase the system ROI. - Reduced IT cost
There is no need for a local server to store the data, which reduces the initial cost of implementation and eliminates the hidden costs of server maintenance. - Automated vibration analysis
By automating the vibration analysis and diagnosis, TurbinePhD greatly reduces the resources required to implement condition monitoring. Instead of relying on highly skilled analysts to interpret vibration spectrum, the system automatically detects and diagnoses the fault, so you don’t need to train and maintain an internal monitoring team or pay a third party to do it for you. This efficiency significantly reduces the lifetime cost of the system.
Features
Universal Anemometer Channels
- Six counter channels accommodate the NRG Class 1 anemometer, the NRG #40C anemometer or the brand of your choice.
- Counter channels do not require SCM cards.
Three ‘Flex’ Channels
- Can be used as either analog or counter sensor inputs.
- Flex channels configure automatically based on the SCM installed.
- Allows for numerous sensor configuration options.
Data Collection
- One-second sampling (conforms to IEC 61400-12-1).
- Data is averaged over a 10-minute period.
- Data is written to an SD Card on an hourly basis.
- Symphonie Data Retriever (SDR) software is available for download.
Password Protected Access
- Unauthorized user lockout prevents access to logger via the keypad.
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