Predicting the future isn’t what it used to be. It’s actually way better, and that’s great news for manufacturers.

This is definitely the case for maintaining equipment on an assembly line. Today, many types of warning systems enable manufacturers to take steps now to prevent dreaded unplanned downtime of robots, conveyors, motors, fans, pumps and other machines.

“到达预测维护的水平是制造商的进化过程，无论其专业如何，” Balluff Inc的全球业务战略经理Hey III指出。“现在，人们对使用传感器改造设备以执行条件非常感兴趣监视作为实施预测维护的手段。下一步是使用具有集成的智能传感器和人工智能的设备。这些技术还可以实现规范性维护，该维护使用机器学习来帮助公司专门调整其经营条件，以确定的生产成果。”

Nearly two years ago, Balluff engineers faced an interesting maintenance challenge: Help a large automotive tier one supplier keep its old, chain-type conveyor up and running. The conveyor features multiple synchronized drives, is nearly 2,000 feet in length, and moves large metal structural components across the whole width of the plant, according to Healy.

“Because the drives are fully synchronized, when one goes down, the conveyor chain buckles and crashes, causing major production downtime,” explains Healy. “We installed a sensor on each drive to monitor its vibration exposure. When excessive vibration in the gearbox alerted maintenance to a drive failure, the manufacturer performed a controlled shutdown, thereby preventing a major machine crash.”

Equipment maintenance has come a long way in a long period of time—from the Industrial Revolution to the Industrial Internet of Things (IIoT). The standard approach for many decades was reactive maintenance, or fixing things after they start to negatively impact production. Then came preventive maintenance, where the goal is to prevent machine failure (due to fatigue, neglect or normal wear) in between scheduled maintenance times.

Now it’s time for manufacturers to enjoy the many benefits of predictive maintenance. Through standard sensors and AI-based software, companies can maximize equipment uptime, target specific components that need attention (resulting in more time-efficient maintenance) and lower equipment lifecycle costs through improved performance and extended equipment life.

Currently, a small percentage of manufacturers have implemented predictive maintenance. But, it’s safe to say that that number will increase when non-practitioners find themselves losing market share to competitors that offer higher throughput and make better products.

Up-and-Running Robots

在机器人方面，获得预测维护的众多好处的关键是连接性。没有它，就无法从控制器，机器人臂和武器末端工具中嵌入的各种传感器获得实时，过程相关的数据。

IIOT的首次机器人预测维护应用之一是几年前在汽车行业中，当时通用汽车与Cisco和Fanuc America Corp.合作，启动了零停机时间计划。预测分析服务称为ZDT，确定了潜在的故障，因此工程师和工厂经理可以安排维护和维修。这样可以防止生产过程中意外的崩溃，从而节省了制造商的时间和金钱。

“ZDT works well because FANUC is vertically integrated,” explains John Tuohy, national account manager at FANUC. “The service also uses binary code for quick and simple calculations. Before introducing ZDT, we provided manufacturers a few weeks advanced notice for required maintenance. Today, we provide as much as much as six months notice, and schedule maintenance at the customer's convenience.”

According to Tuohy, the ZDT program has proven to be quite successful over the last several years. He says that about 30,000 robots worldwide are connected to the system.

Automotive manufacturers represent the largest group of ZDT users. Others include companies in the aerospace, white goods, packaging, and food and beverage industries.

“The objection I hear from customers that are hesitant to invest in ZDT is that they don't need it since their FANUC robots work so well and often exceed the suggested mean time between failures (MTBF),” notes Tuohy. “For our industrial robots, the MTBF is 100,000 hours or 10 years, while, for cobots, it’s 80,000 hours or 8 years.

Tuohy继续说：“但是，我们向最终用户指出了ZDT的另一个好处，ZDT是其提取ERP过程数据以准确确定生产吞吐量的能力。”“通过利用这些数据，制造商可以确保其机器人最佳地运行。”

Within each customer’s plant is a ZDT data collector, or a virtual machine, that securely transfers messages from the robots to FANUC’s ZDT data center in the cloud. There, FANUC's analytic programs carefully review the data for any potential problems.

Tuohy说：“如果出现统计异常，ZDT会自动通知我们的服务团队，并为客户提供建议的措施来确认和纠正问题。”“无论客户是制造商还是集成商，获得这种见解都足以提高机器人的生产率。”

For customers with operations in multiple states or even overseas, ZDT combines robot data from all locations into one dashboard so managers and engineers can remotely check equipment status and support local maintenance personnel. According to Tuohy, the more FANUC knows about what’s going on inside a robot, the better it can understand all of the elements around the robot that affect a manufacturing process.

He also acknowledges that manufacturers that implement ZDT may or may not need to replace their older FANUC robots. This is because many older models can be enhanced with ZDT, but newer models are better able to maximize its predictive maintenance capabilities.

KUKA Robotics offers its own cloud-based data analytics and intelligence platform, which is called KUKA Connect. It monitors things like arm speed and load, and predicts how projected maintenance cycles are affected by various assembly applications.

Other benefits include being platform agnostic, vertically scalable, fast reacting and easy to operate. The latter capability stems from its web interface that requires no software to be installed, and connects to any mobile device from anywhere at any time.

A subscription-based platform, KUKA Connect enables manufacturers to take advantage of comprehensive production data, innovative manufacturing processes and flexible networking components. The Lite version of the platform provides access to information about each KUKA robot’s functionality. Upgrading to Connect Plus gives users full access to real-time notifications and exportable reports.

传达关键信息

“输送机用户时,有两个选项predictive maintenance,” explains Mike Hosch, vice president of industrial business at Dorner Manufacturing Corp. “The most popular approach is to mount third-party sensors at various points on each conveyor and have the sensors feed real-time data to a PLC or master device. The types of data obtained are often related to belt speed, hours of operation, motor vibration and amperage draw, and bearing temperature.”

Less popular among manufacturers, at least to this point, is what Hosch calls ‘Level 2’ predictive maintenance. At this level, one or more conveyors are equipped with integrated sensors, which work in conjunction with software or artificial intelligence to provide performance data and more. This advanced data-gathering setup also enables the plant manager to easily and continuously monitor all conveyors in a facility on an iPhone or other smart device.

Hosch指出：“要增加实施2级预测维护的制造商的数量，输送机供应商必须面对两个大挑战。”“有人说服客户，即使传感器集成的输送机的成本更高，它们将在短期和长期内增加生产时间。”

同时，供应商在使用此类技术高级输送机时必须减少客户的数据安全问题。根据Hosch的说法，这需要回答客户IT工作人员与数据采样相关的关键问题。例如，每次5秒，30秒，2分钟等一次，每次数据的安全程度如何？

“预测维护类似于预防in that both require maintenance personnel to pay regular attention to conveyors,” says Hosch. “The real difference is the predictive model requires that the maintenance staff be better trained.”

Hosch says that, ideally, training should include more than one on-staff employee. This way, if a conveyor goes down, at least one person with proper training is always available. Another good practice is making sure all maintenance staff is present whenever a conveyor audit is performed and predictive maintenance is implemented.

Hosch acknowledges that manufacturers sometimes add external sensors to the Dorner 2200 and 3200 series of belt conveyors, even though they require low maintenance from day one. This is due to both models featuring precise rack-and-pinion belt tensioning and sealed-for-life bearings. The 3200 series also offers modular and spliced standard belts that allow for quick belt changing.

In 2020, the global data analytics and advisory firm Quantzig implemented predictive maintenance for a European conveyor-belt manufacturer and service provider. Quantzig was hired to help the company make sense of the large volumes of data it received from customers who obtained it from sensors mounted near belts in operating conveyors.

The client wanted to switch from reactive to predictive maintenance to curtail risks and identify belt-performance problems before they occur. Quantzig helped them achieve this goal in three steps.

The first was teaching them the importance of establishing and monitoring all assets’ conditional baselines. Quantzig’s experts then harnessed data from every sensor and created time-series modeling combined with machine learning.

Finally, Fourier and Support vector machine algorithms were used to transform the data into a predictive analysis that generated specific maintenance alerts and recommendations. The end result of all this work is one that makes the manufacturer quite happy: A four-year increase in wear life, on average, for each belt it makes.

Monitoring Other Machines

The performance of a wide range of assembly line equipment, besides robot and conveyors, can be optimized through predictive maintenance. This equipment includes motors, pumps, fans and compressors.

Balluff offers three products that let manufacturers implement predictive maintenance through machine retrofitting. The company’s IO-Link sensors, for example, bolt onto any machine and obtain real-time data related to operating hours and remaining service life. Healy cites CNC machines, stamping presses, gantry cranes and welding exhaust fans as common applications.

这些IO-Link传感器也可以作为of a Condition Monitoring Toolkit, which includes software and a base unit. Data visualization and plug-and-play commissioning are possible with the kit, so long as the retrofitted machine has 24-volt power and a plant network connection point.

Product three is a cloud-based Portable Monitoring System. It contains the BCM condition monitoring sensor, a mobile gateway for data transmission via mobile radio, and software for visualizing data on any terminal device. Healy says the system comes preconfigured to digitally and efficiently monitor pumps, fans, motors and machine tools.

“An important factor in understanding predictive maintenance is the P-F curve, or the three-domain interval between the detection of a potential failure (P) and the occurrence of a functional failure (F),” says Healy. “In the proactive domain, the failure is relatively far off, as the machine may still be new. The failure may still be far off in the predictive domain, but symptoms are emerging with relatively early warning signs. Timely action may be taken, such as replacing failing equipment, before catastrophic failure occurs. Without action, one enters the fault domain, where failure is occurring or inevitable, and symptoms indicate immediate action is needed.”

Festo自动化体验（Festo AX）软件中的模块依赖于基于AI的条件监控来实现组件，机器和系统的预测维护。Festo Corp.电动自动化产品经理Frank Latino表示，维护模块使公司可以使用设备和传感器从气动和电动执行器，阀歧管，阀歧管和开关的气动夹具中收集数据，这些夹具可在工作站中保存零件。

“Festo AX requires a short AI training phase and the input of system algorithms, along with human input, into the software’s analysis function while data is collected from the factory floor,” explains Latino. “This data is evaluated either at the edge of a network, or in the cloud using Microsoft Azure, Amazon Web Services or other similar services.”

According to Latino, the main benefits of the Festo AX Maintenance module are greater uptime and a 20 percent reduction in time spent doing maintenance. Root cause analysis also helps manufacturers specifically identify the systemic causes of each maintenance problem.

一般而言，运动温度通常是机器或装配线运行效率的良好指标。例如，运动温度的20度尖峰可能表明需要维修或更换一些机械传输组件。

Yaskawa AC drives control motors. They also simultaneously use visualized data to detect anomalies and predict machine failure via the motors.

一些公司使用一个或多个驱动器来监视装配线上的粉丝。在这种情况下，驱动器不断监视并检测到过滤器堵塞和累积污染的当前量。必要时，驱动器通知操作员以清洁过滤器。

Pumps and compressors can be monitored as well. Air entrainment in, and dry running of, a pump can cause the drive to notify the operator of a possible failure due to either condition. As for compressors, liquid return on freezers or chillers on which a compressor is mounted will often change the input frequency pulsation. Should this happen, the drive will tell the operator that it’s time for compressor maintenance.

“Companies often keep equipment much longer than their stated service life, so there’s definitely a benefit to implementing predictive maintenance,” says Healy. “Machines that are made to work 5 to 7 years, sometimes end up being used for 25.

Healy总结说：“通常，工人和维护专业人员的短缺应该增加预测性维护的实施。”“但是，真正将其前进的是，当植物或中级经理推广植物或中级经理时，具有影响力和预算权限的高管推动了它的实现。”