To understand the issues surrounding the two techniques, think of machine health as analogous to our own health. Visiting the doctor for a yearly physical is a common way to assess your overall wellness and to predict future health issues. Imagine you go to your doctor’s office and after sitting in the waiting room for an hour, you finally see the doctor who checks your temperature and only your temperature. To be thorough, she checks the temperature at multiple locations on your body and gives you a diagnosis of good health. With no other conversation, she sends you on your way, after you have already paid, of course. In this scenario, everything seems viscerally wrong from a healthcare standpoint. But this is the exact approach many companies take with machine health. They outfit their machines with only accelerometers and use only vibration to monitor the machine’s health similar to the doctor only checking your temperature as an indicator of your overall health. Although this is a great indicator of wellness, it is not the only one. The practice of using a limited number of diagnostic tools is a problem for traditional methods -- both manual route-based and automated. The two approaches fall short, either because of the route-based technician’s lack of expertise to measure and analyze other sensors or the measurement platform’s lack of flexibility in integrating or expanding to new or custom sensors.
Remember that the ultimate goal of this journey is to gain a holistic view of your overall health
Now back to the doctor analogy. You decide to do your due diligence and ensure that you actually are in good health. You continue your physical evaluation by visiting another doctor who can measure your blood pressure and cholesterol. Again, you have to pay this doctor and the only thing you receive is a diagnosis based on the narrow scope of your blood pressure and cholesterol. As silly as it sounds, this mimics the real-world approach to traditional machine health assessments. Maintenance managers try to give their machines a more complete health diagnosis but are left with a less than holistic machine health assessment. This is a result of disparate monitoring systems being cobbled together to take the measurements. In the end, just like the case of going to separate doctors, this makes it costly and difficult to scale a monitoring solution across all assets because of the high up-front costs of the initial system, the cost of adding on subsequent systems and then integrating everything together.
On the opposite end of the spectrum, companies can perform manual measurement rounds, which are less expensive in theory, but, in reality, cannot be scaled to cover a large number of assets. The technical prowess required to take and analyze measurements coupled with an aging workforce prevent companies from solving the problem by indiscriminately placing more people on it. Even if this weren’t the case, there are no economies of scale to be gained with this method. Monitoring five times more assets would result in five times the cost and even more logistics. Thirty people performing 60,000 rounds per month to cover 2,000 assets could suddenly become 150 people performing 300,000 rounds per month to cover 10,000 assets. Why? Because people don’t scale. Adding different sensors results in even more people, because of the expertise needed for the different measurement specialties. The very nature of this work becomes incredibly inefficient. Specialists can spend up to 80 percent of their time manually collecting the data with only 20 percent of their time left to actively analyze the data and uncover root-cause issues that prevent costly repairs in the future. And because it’s manually collected by a variety of people, there is the potential for dirty, disparate data.
There needs to be a better approach to condition monitoring
To conclude our analogy, remember that the ultimate goal of this whole journey is to gain a holistic view of your overall health. After visiting multiple doctors and gathering multiple diagnoses, you would be frustrated, to say the least. Each doctor used a separate tool to assess your health and the ability to integrate all of your health data did not exist. As a result, you would not have a holistic or accurate assessment of your health because the doctors couldn’t be brought together to communicate their finding and give an accurate diagnosis. Added all together, your physical was inconclusive and your time and money was wasted. This wouldn’t be because the doctors didn’t want to deliver a conclusive review of your overall health, but rather that they are limited by their roles, their instrumentation and their ability to communicate the data with each other or you, the patient. When dealing with separate monitoring systems (and sometimes manually entered data) this is all too often the case. Not only do the systems not talk very well with one another and the enterprise, but there also isn’t an option for you to perform your own analysis because there is no access to raw data.
Overall, traditional approaches present problems in four main areas:
- Flexibility – Integration with a multitude of sensors
- Scalability – Financial and logistical possibilities to expand to cover all assets
- Accessibility –Raw data that can be easily integrated and analyzed on an enterprise level
- Cost – Capital expenditures of the end-to-end solution.
There needs to be a better approach to condition monitoring. It should provide a holistic view into the health of the machine, be cost-effective enough to be applied to more assets, and be flexible enough to evolve with new sensors and techniques for machine monitoring.