The demands on industrial data collection systems have continued to grow year over year for advances in speed, labor cost reduction, error proofing, maintainability, flexibility, accuracy, training efficiency, and exception reporting. To complicate matters, while there are similar applications, there are no generic “cookie cutter” applications. Virtually every corporation and plant site has one or more unique requirements for attribute, variable and traceability data. Addressing these demands in this challenging environment requires a flexible, modular approach that can be sized and featured to fit. To better understand the scope of present day industrial data collection, let us look in depth into a wind turbine field maintenance application, an automotive residual torque audit, a consumer product barcode verification application, a net weight lot release application, and a non-conformance management application.
Scheduled maintenance on wind turbines is very challenging and somewhat dangerous work. The work takes place hundreds of feet in the air. Key requirements for this application are a check list of work to be performed, direct gage interface for gage and/or torque measurements, a checklist of equipment to be removed upon completion, and transfer of the maintenance record to a central database. The maintenance checklist itself must clearly identify each task, the equipment needed, and the procedure to follow. The scheduling software must be cognizant of the maintenance needs and history of each individual turbine. For example, the maintenance needs of two identical turbines will differ if one is located inland, and the other is subject to ocean salt spray. Direct gage interface is a key enhancement as it speeds the data collection task and completely eliminates gage reading errors and keyboard entry errors. The equipment to be removed checklist upon completion is no mere afterthought. A tool left in the turbine could fall, potentially causing turbine failure. The maintenance person needs access to two reports before leaving the worksite. The first report highlights any missed maintenance checks, and the second report highlights any forgotten equipment. If any issues are identified with any of the components checked in the turbine, it is of high importance that the producer of the turbine trace any components with similar issues to perform preventive maintenance or start an investigation into the cause of the problems.
The automotive residual torque audit is conducted to ensure product safety. An under torqued steering gear fastener could result in a tragic vehicle crash. These audits are highly labor intensive. The typical automotive assembly plant may have eight to ten auditors who each make 500 or so torque measurements on roughly 100 different fasteners per shift. The basic requirements for the application are an unambiguous identification of which fastener to measure, inspection plan sequencing to minimize keystrokes, easy navigation when multiple models are assembled on the same line, immediate nonconformance reporting, accurate residual torque measurements, and audit completion reporting. The advent of graphical prompting has effectively eliminated the problem of measuring the wrong fastener, while reducing training time. Angle based residual torque technology that captures residual at the start of fastener retightening motion has completely eliminated false high readings due to excessive overshoot and false low readings due to premature wrench release as well as eliminating operator dependency and high static friction as factors that plague older analog peak based measurement systems. Besides collecting individual torque readings, the advanced data collection system can capture torque/time and torque/angle curves that document joint behavior and can help differentiate material problems from installation problems.
The typical residual torque auditor has a single job function. It is to measure residual torque on fasteners and report nonconformance. Other plant personnel are responsible for troubleshooting the cause of the nonconformance, making corrections, quarantining and clearing a suspect lot. In order for others to be able to follow up on nonconformance the data collection system needs to be able to report the operation number where the nonconformance occurred, the serial number of the power tool, the engineering control number, the specification limits in force at the time, the suspect lot delimiters, and the team and/or group leader responsible for follow up. Since failure to audit a particular fastener is both a safety concern and a liability risk, the data collection system must be able to report those fasteners that were not audited at the required frequency during each shift. The capability to send the data to a centrally accessible database is of critical importance for traceability and R&D work. The torque application is only one of many quality gates a vehicle goes through before release from the factory. By aggregating other quality data in a common database, the cost of training, maintenance, and plant to plant comparison is reduced.
Barcode verification is conducted at every consumer product filling line that fills more than one product over time. Conceptually the application is very simple: just scan the barcode on the container. In practice it is a bit more complicated. For example, to report nonconformance the data collection system needs to know that when filling a standard cola product a UPC barcode on the container indicating diet caffeine free product is a reason to stop the line. Right now! Mislabeled edible consumer product at best may be used as animal feed. At worst it takes a trip to a landfill.
Data collection for food product net weight applications is much more than just recording a series of weight measurements. The USDA requires that the average weight of a lot must be at or above the label weight, and that no individual container be below label weight by a certain amount. The allowable individual underfill for a homogeneous product like bottled water is different than for a heterogeneous product like canned beef stew. In line check weighers handle rejection of individual underfills. As valuable as in line check weighers are for individual underfill rejection they are not accurate enough to perform all required aspects of net weight control. Many times, the economic impact of average lot weight is established using precision balances. The name of the game in net weight applications is to never underfill, but at the same time minimize any overfill to the lowest economically safe level. To that end, the data collection system needs to recommend fill adjustments based on the periodic samples, the current total overfill, and the amount of the run yet to be filled. Think of the opportunity. What would an overfill reduction of one-tenth percent mean to your company?
Data collection for non-conformance management presents another set of challenges, or to be more precise, three sets of challenges. All companies have a minimum of three different types of complaints to deal with. Supplier, internal and customer complaints all have a specific workflow and required actions and documentation. It is important that the initiator of a complaint is guided through the process and that the needed information is collected to ensure that the case handler later in the process can perform required actions. Having all the data for the three types of complaints allows for advanced root cause analysis and eventually leads to time and cost savings. It is important that the system not be an “island of automation,” but interface directly with the ERP and MES systems in place.
In conclusion, an advanced quality system will support virtually every conceivable quality related data collection and reporting application. It is important that the system not only capture the quality data while producing, assembling and installing the product or service but also is capable of quality planning when preparing for production. The need for transparency among different production sites, often on several continents operating with different languages and different regulatory regimes adds to the demands on the data collection system. Not only must the data collection system be capable of multi-site and multi-country operation, timely local service and support is vital for trouble free operation. When choosing a data collection system vendor it is important to weigh not only the needs of the moment, but likely future growth.
An advanced quality system will support virtually every conceivable quality related data collection and reporting application.
It is important that the system not only capture the quality data while producing, assembling and installing the product, but also is capable of quality planning when preparing for production.
The need for transparency among different production sites adds to the demands on the data collection system.
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Paul Oxfeldt is director of corporate sales development at ASI DataMyte. For more information, visit www.asidatamyte.com.
Frank Skog is an application engineer at ASI DataMyte. For more information, visit www.asidatamyte.com.