Data collection and analysis can be used in a variety of ways, but for this column I would like to focus on maintenance. Though maintenance may not seem as exciting as all the other production improvement possibilities, I find maintenance to be an often overlooked aspect of manufacturing which can lead to costly, unscheduled downtime and quickly swamp any incremental gains in production improvements.
Maintenance programs tend to fall into one of three categories:
Fix It When It Breaks.This barely qualifies as a program, but there are many folks out there who operate in this fashion. It represents a very expensive way to run a manufacturing facility, as virtually every maintenance event results in that undesirable, expensive unscheduled downtime.
定期保养。This is the most common maintenance program and consists of periodically servicing equipment based on an educated guess as to how long it takes things to wear. This period is generally settled on over time, meaning you have to be burned a few times by unscheduled downtime before you are able to settle on a period that takes into account all the failure modes of the piece of equipment. The problem with periodic maintenance is that, to be effective, the period is based on the worst-case scenario and you end up over-maintaining your equipment most of the time.
Preventive Maintenance.This is becoming more common. We are hearing it being considered mosy often in applications where the control system is used to monitor events in the system (e.g., motor runtime, counting strokes on a cylinder, or cycle counts on contactors), which is then cross-checked to manufacturers’ recommended maintenance schedules which triggers maintenance for that piece of equipment. This represents a great maintenance program, but I would suggest it still leaves room for improvement—as manufacturers’ specifications are generally conservative and do not always take into account the application of the equipment.
Looking at these three common practices, there are some very obvious holes ... which means there is significant opportunity for uptime and cost improvements with a better way. The better way is主动维护。这里的想法是将传感器和技术添加到设备中，以预测问题并在任何真正的麻烦之前触发维护。传感器，网络和处理技术的进步已使这成为可能。大多数应用程序都有一个传感器，网络它们是微风，并且内存便宜，因此存储数据是一个简单的命题。然后，它成为构建监视/通知系统的问题，我建议任何信誉良好的自动化解决方案提供商都可以处理。
这种类型的维护数据收集的一些直接示例包括：轴承集的振动监测器，监测电动机温度/电流，跨滤波器的压力监测以及链/输送机上的张力监视。有了这些数据，建立一些设定点/公差就可以向适当的人员发出通知。在某些应用程序中，可能需要更高级的监视，以驱动对统计过程控制（SPC）系统或其他高级算法进行预测故障的需求。无论哪种方式，通过适当的设计和考虑，都可以为任何设备建立标准，以确保在正确的时间进行维护，而不是基于任意时间/周期计数。
Now to take things a little further, with the “Internet of Things” concept, notifying the proper personnel can be more than a pop-up window on your maintenance supervisor’s desk. What about automatically generating a purchase order to your local vendor for the parts that are going to be required for the work? How about an order automatically emailed to your local service provider to schedule the work? This represents a significant deviation from the standard way of doing business, but the technology exists to handle maintenance and service in this fashion, essentially connecting the data on the plant floor to the people that can take action on it.
With the right information, well ahead of a failure, you can make better decisions on how to handle an equipment problem rather than reacting when the pressure is on. Like I said, there is informational gold in all that data, it is time to start digging and collecting the data.
Michael Gurney is CEO of概念系统公司，一名认证成员Control System Integrators Association. See Concept Systems’ profile on the工业自动化交换.
