Sensors are used to determine and monitor status and conditions of equipment and the environment at that equipment. For example, a sensor may be used to monitor temperature, humidity, atmosphere at an environment or other ambient conditions. Other sensors are used to monitor physical parameters of equipment, and the status of the equipment itself, including determining strain, vibration, and development of cracking.
Sensors also may be used to log data. Powers et al. in U.S. Pat. No. 5,381,136 (1995) describe a remote logger unit for monitoring a variety of operating parameters along a fluids distribution or transmission system. An RF link is activated by which a logger unit alerts a central controller when predetermined operating limits are exceeded. Farther logger units transmit data via closer logger units in daisy chain fashion.
Arms in U.S. Pat. No. 6,588,282 (2003) describes peak strain linear displacement sensor for monitoring strain in structures. The device records data and can report strain history for the structure to which it is attached. A displacement sensor is constrained so that it shows maximum movement in one direction resulting in deformation of the structure to which it is attached.
Hamel et al. in U.S. Pat. Nos. 7,081,693 (2006) and 7,170,201 (2007) describe devices for powering a load by harvesting energy as electrical energy from an ambient source, storing said electrical energy, and switching the storage device to provide electrical energy when required to a load such as a sensor. The example is provided of powering a sensor for monitoring tire pressure and transmitting that data.
Arms et al. in U.S. Pat. No. 7,696,621 (2010) and in a conference presentation, “Wireless Strain Sensing Networks,” 2nd European Workshop on Structural Health Monitoring, Munich, Germany, Jul. 7-9, 2004, describe a RFID tag packaging system for an electronic device located within a cavity in an adjacent flexible material. The dimensions of the flexible material are chosen so as to provide protection of the electronic device from loading applied to the device.
A preferred approach to maintenance is “Condition-Based Maintenance” (CBM). Equipment downtime, both scheduled and unscheduled, is an important factor of production loss. In addition, according to a study by Optimal Maintenance Decisions Inc. (OMDEC), a leader in condition-based maintenance (CBM) management solutions, failures in the field are three times more costly to repair (considering overtime, rescue, and expedited shipping of parts) than scheduled (or preventive) maintenance operations.
Hence, CBM is replacing preventive maintenance in many industrial operations as a result of gains in productivity. Condition-based maintenance (CBM) is a maintenance system prevalent in industrial mining and energy operations. CBM monitors equipment to establish an optimal maintenance cycle (based on the predictions of when a machine will fail using strain and vibration measurements, for example).
While preventive maintenance repairs machinery every given time period, even if the machine is still operational, CBM can extend that time period. The optimal maintenance cycle determines the best time to shut down a machine for preventive repair. Finding the balance between repairing often and continuing to produce is the tricky part.
As an example, strain and fatigue measurements reveal risks of yield failures and cracking, changes in material properties, and remaining equipment life, making them incredibly useful for CBM if monitored.
In the mining industry, strain and vibration are not generally monitored by built-in systems, mainly due to the complexity of sensor installation and computational intensity of the data processing. CBM relies heavily on regular or continuous measurements of parameters that allow operators to determine when the machine will fail (i.e. strain and vibration).
Electronic sensors measure physical quantities (such as strain, temperature, acceleration, crack propagation, pressure, etc.) and convert them into signals read by an instrument (the reader varies depending on the type of sensor).
For example, strain gauges consist of a foil pattern (often in a tight zigzag) insulated in a flexible material and attached to an object under strain. As the object deforms, the resistance of the foil wires changes, allowing a Wheatstone bridge circuit (a measuring instrument used to measure an unknown electrical resistance) to record the variations.
Unfortunately, existing CBM solutions have been historically inaccurate, are expensive or non-viable, and/or produce poor signal transmission and short battery life.
CBM's reliance on high data volume dictates a need to monitor continuously (or at least often) strain and loading. To understand fully a machine's state requires monitoring of cracks and crack growth. However, monitoring the hundreds of machines used every day in a mining operation requires many sensors and many more wires, which are difficult and expensive to install and maintain.
Many solutions have not reliably predicted when a machine will fail. This parameter is probably the most important when it comes to CBM, since CBM relies on accurate predictions of failure. The inability to predict correctly when a machine will fail can have grave consequences on unplanned downtime as well as operator safety.
Some solutions offer accurate predictions, but at high costs, whether in the stages of installation and setup, longevity and data collection, or analysis and data post-processing.
Yet other solutions offer poor signal transmission due to low range or lack of direct line of sight. Power supplies dictate operating conditions and longevity of the solution. Most solutions require too much power to operate for long periods of time, or are too delicate to operate in the harsh conditions of mining operations. Conditions can include extreme temperatures, constant vibration, and quick acceleration.