First of Two Parts
According to the U.S. Energy Information Agency, growth in power generation is expected to increase 8 percent while demand for electricity is expected to grow 8 to 9 percent from 2011 through 2020.
Compounding this growth are aging plants with critical equipment at the end of its life—increasing demands for reliability—and an aging workforce reaching retirement in the next few years. All these factors exponentially increase the need for effective and automatic knowledge transfer, training and new approaches to the maintenance of power generation assets. Today, the process of condition monitoring is largely conducted manually, meaning technicians and operators monitor equipment on their walking rounds or tours within a plant (Figure 1). This includes capturing data logs, inspections and assessments, performance testing, maintenance, and capturing history and events. In addition, this provides limited access to equipment condition monitoring.
This article discusses how reliability and maintenance engineers can use technology to improve existing maintenance programs. Most often, equipment failures can make the
difference between generating a profit or a loss. However, increased inspection through online monitoring and data collection can mitigate these risks.
To optimize machine maintenance and, therefore, machine reliability and use, monitoring health indicators such as mechanical vibration, temperature and power factor is a widely accepted practice. However, the cost of cabling the sensor and data acquisition hardware to the control room has impeded the use of monitoring for reliability and usage improvements. Today, with the use of wireless vibration and power monitoring devices, reliability engineers can overcome historical cost barriers.
Power generation providers are taking advantage of the cost effectiveness of wireless devices to add low-cost sensors to equipment. Without the need to connect wires to transfer data, reliability engineers can expand instrumentation beyond critical assets and communicate condition monitoring data for many assets across systems.
The Electrical Power Research Institute (EPRI) has calculated comparative maintenance costs in U.S. dollars per horsepower for each maintenance strategy. According to the research, a scheduled maintenance strategy is the most expensive to conduct at $24 per horsepower. A reactive maintenance strategy is the second most costly at $17 per horsepower and includes the additional costs of safety being compromised. A predictive maintenance strategy is the most cost-effective at only $9 per horsepower, and it nearly eliminates the risks of secondary damage from catastrophic failures.
Conversely, one of the consequences of the development of advanced maintenance strategies, such as predictive maintenance, is the increased need for efficient information/data management methods. As an example, advanced predictive maintenance strategies require significantly larger data sets to monitor assets and effectively determine the actual state of the asset being monitored. These data sets include machine parameters, measuring points, failure modes to be detected, the relationship between faults and symptoms, and real-time math calculations.
As a result, acquiring, analyzing and managing this massive amount of data, efficiently and timely and communicating operations knowledge throughout the organization becomes a complex task.
Types of Asset Monitoring
Each of the five main types of machine condition monitoring serves a different role. These five are described below:
- Route-based monitoring involves a technician recording data intermittently with a handheld instrument.
- Portable machine diagnostics uses portable equipment to monitor the health of machinery from sensors that are typically permanently attached to a machine.
- Online machine monitoring monitors equipment as it runs. Data are acquired by an embedded device and are transmitted to a main server for data analysis and maintenance scheduling.
- Online machine protection actively monitors equipment as it runs. Data are acquired and analyzed by an embedded device. Limit settings can then be used to control turning the machinery on and off.
Sensors and Signals
The following types of sensors dominate machine condition monitoring:
- Accelerometers are used to monitor the vibrations of a machine.
- Proximity probes monitor the movement of a shaft and detect imperfections, such as faulty bearings or other external factors preventing perfect rotation.
- Tachometers determine the equipment’s rotational speed and phase information, so engineers can match frequency components to shaft speed and position.
Most machine condition monitoring sensors require some form of signal conditioning to optimally function, such as excitation power to an accelerometer. Filtering on the signal to reduce line noise and unwanted frequency ranges is also common.
Asset Monitoring Benefits
Implementing an asset monitoring system provides other advantages in addition to cost savings. For example, organizations can plan replacement parts inventory to meet maintenance demands by ensuring that the correct parts are available at the right location as needed, ensuring better fleet management. Also, with a longer maintenance cycle based on machine health, a longer equipment life span can be expected.