The gang saw is a critical part of the lumber production process.  Its function is to cut cants into smaller sizes according to the merchandising prescription set by market demand.  Gang saws run constantly at high speeds but are only under load when cutting, causing high levels of damage. Motor and arbor bearings of the gang saw are extremely vulnerable to vibration damage due to high-speed operation, repeated loading and unloading cycles, and arbor misalignment or flexing. 

The components of the gang saw are difficult to monitor because accumulation of large amounts of dust and debris can interfere with conventional monitoring equipment.  Failures associated with motor bearings and couplings, arbor bearings, and the arbor itself can cost a mill over 2 million dollars annually in lost production and repairs.

There are several challenges to effectively monitoring the health and operation of gang saws. Due to the difficulty in accessing critical monitoring points, the need to capture data when the motor and arbor are under load, and large data

1. Saws are not under constant load, making it difficult to obtain reliable and consistent vibration measurements for analysis. 

2. Traditional route-based monitoring (if even possible) is time consuming, unreliable, and can expose the technician to unnecessary risks and dangers.  

3. Route-based monitoring results in large time gaps in data collection, where the health of the asset and onset of damage causing conditions is unknown.


Frequent loading and unloading of the motor and arbor misalignment or flex cause extremely high levels of damaging vibration and heat, which can cause the motor bearings and couplings to fail.


The arbor is the rotating shaft upon which the saw blades rotate.  Arbors that are misaligned or flex during cutting cause enormous stress to the arbor bearings resulting in excessive loading and failure. 


Misaligned arbors and/or arbors subject to excessive flexing can cause premature blade wear, poor product quality, and can even cause the arbor itself to break.


Finally there’s a solution to capturing regular, consistent, and simultaneous data points during the most critical time of the saws operation.

The IoT Hub from KCF uniquely addresses the challenges of monitoring a gang saw’s critical components.  It enables the user to collect simultaneous data samples from each monitoring point and transmit the data wirelessly to a KCF Base Station.  The data can then be displayed in SMARTdiagnostics, enabling real-time and historical trending of data and development of actionable alarms to alert the operator to developing damage causing conditions. Data can be integrated through the connecting to the PLC so that each data sample can be taken at the same time when the motor and arbor are cutting under load.

Consistent measurements during load cycles will enable far more reliable data to diagnose developing damaging conditions and plan appropriate corrective action. The operator and maintenance teams will have the data they need to immediately determine if repairs and maintenance were performed correctly.




The Hub’s sensors are located directly on the system components that are identified as most problematic, including the motor and arbors. Analysts begin with the standard vulnerabilities and work with the operations and maintenance team to tailor it to the individual application.


While the IoT Hub is a wireless monitoring solution, its design allows for remote placement of the radio transmitter.   It can be kept clear of production waste accumulation that can interfere with data transfer.


The system is configured to acquire data at the precise time when the known failures or damaging conditions occur.  This typically would require a complex wired system that is cost prohibitive for most applications but the Hub Hub makes this affordable and relatively easy to install to an existing PLC.


KCF analysts will train the maintenance and operations team, providing provide guidance regarding best practices for operation and maintenance to extend system life. 


All machines and processes experience change and are subject to variability in maintenance, wear, and operational variability. KCF works to adapt the solution, constantly learning of new vulnerabilities and updating data acquisition and sensor configuration to enable continuous improvement.


Root cause analysis allows KCF engineers to suggest changes to reduce risk factors.  These can include changes to speed of blades, feed rate, and other factors.  The real-time monitoring data can be programmed to feed information directly to system controls.

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