Accelerometers and vibration sensors are having their day. As prices have come down drastically, we are seeing more and more companies instrumenting all kinds of devices and equipment. Industrial, automotive and consumer products use cases are proliferating almost as fast as startups with “AI” in their names.

加速度计和振动传感器正在兴起。 随着价格的急剧下降，我们看到越来越多的公司在检测各种设备。 工业，汽车和消费产品用例的增长速度几乎与名称中带有“ AI”的初创企业一样快。

In many cases, particularly in industrial applications, the purpose of the new instrumentation is to monitor machines in new ways to improve uptime and reduce cost by predicting maintenance problems before they occur. Vibration sensors are an obvious go-to here, as vibration analysis has a long history in industrial circles for machine diagnosis.

在许多情况下，尤其是在工业应用中，新仪器的目的是通过以新的方式监控机器，以通过在出现维护问题之前预测维护问题来改善正常运行时间并降低成本。 振动传感器是显而易见的去向，因为振动分析在工业界用于机器诊断的历史悠久。

At Reality AI, we see our industrial customers trying to get results from all kinds of sensor implementations. Many of these implementations are carefully engineered to provide reliable, controlled, ground-truthed, rich data. And many are not.

在Reality AI，我们看到我们的工业客户试图从各种传感器实现中获得结果。 这些实现中的许多实现都是经过精心设计的，以提供可靠的，受控的，真实的，丰富的数据。 许多不是。

使用加速度计和振动 (Working with accelerometers and vibrations)

In vibration data, there are certainly things you can detect by just looking at how much something shakes. To see how much something is shaking, one generally looks at the amplitudes of the movement and calculates the amount of energy in the movement. Most often, this means using measures of vibration intensity such as RMS and “Peak-to-Peak”. Looking at changes in these kinds of measures can usually determine whether the machine is seriously out of balance, for instance, or whether it has been subject to an impact.

在振动数据中，肯定有一些东西可以通过仅查看晃动多少来检测到。 要查看有多少东西在晃动，通常先看一下机芯的振幅，然后计算机芯中的能量。 大多数情况下，这意味着使用振动强度的度量，例如RMS和“峰对峰”。 例如，查看这些措施的变化通常可以确定机器是否严重失衡，或者是否受到了影响。

For more subtle kinds of conditions, like identifying wear and maintenance issues, just knowing that a machine is shaking more isn’t enough. You need to know whether it’s shaking differently. That requires much richer information than a simple RMS energy. Higher sample rates are often required, and different measures.

对于更细微的情况，例如识别磨损和维护问题，仅知道机器在剧烈晃动是不够的。 您需要知道它是否在发抖。 这比简单的RMS能量需要更多的信息。 通常需要更高的采样率，并且采取不同的措施。

Trained vibration analysts would generally go to the Fast Fourier Transform (FFT) to calculate how much energy is present in different frequency bands, typically looking for spectral peaks at different multiples of the rotational frequency of the machine (for rotating equipment, that is; other kinds of equipment are more difficult with Fourier analysis). Other tools, like Reality AI, do more complex transforms based on the actual multidimensional time-waveforms captured directly from the accelerometer.

受过训练的振动分析人员通常会去进行快速傅立叶变换(FFT)，以计算不同频带中存在多少能量，通常会在机器旋转频率的不同倍数处寻找频谱峰值(对于旋转设备而言，即其他频率)。傅立叶分析更难确定各种设备)。 其他工具(例如Reality AI)基于直接从加速度计捕获的实际多维时间波形进行更复杂的转换。

But rich data brings rich problems — more expensive sensors, difficulty in interrupting the line to install instrumentation, bandwidth requirements for getting data off the local node.

但是，丰富的数据会带来很多问题，例如昂贵的传感器，难以中断安装仪器的生产线，从本地节点获取数据的带宽要求。

Many just go with the cheapest possible sensor packages, limit themselves to simple metrics like RMS and Peak-to-Peak, and basically discard almost all of the information contained in those vibrations.

许多传感器仅采用最便宜的传感器套件，将自身限制在诸如RMS和峰峰值的简单度量标准上，并且基本上丢弃了这些振动中包含的几乎所有信息。

Others use sensor packages that sample at higher rates and compute FFTs locally with good frequency resolution, and tools like Reality AI can make good use of this kind of data.

其他人则使用传感器包，以更高的速率采样并以良好的频率分辨率在本地计算FFT，诸如Reality AI之类的工具可以很好地利用此类数据。

Some, however, make the investment in sensors that can capture the original time-waveform itself at high sample rates, and work with tools like Reality AI to get as much out of their data as possible.

但是，有些公司在传感器上进行了投资，这些传感器可以以高采样率自身捕获原始时间波形，并与Reality AI等工具一起使用，以尽可能多地利用其数据。

这不是矫kill过正 (It’s not overkill)

But I hear you asking “Isn’t that overkill?”

但是我听到你问“那不是过度杀伤力吗？”

Do I really need high sample rates and time-waveforms or at least hi-resolution FFT? Maybe you do.

我真的需要高采样率和时间波形，或者至少需要高分辨率的FFT吗？ 也许你会。

Are you trying to predict bearing wear in advance of a failure? Then you do.

您是否要在故障之前预测轴承磨损？ 那你做

Are you trying to identify subtle anomalies that aren’t manifested by large movements and heavy shaking? Then you do too.

您是否要确定大运动和重震动不会表现出的细微异常？ 那你也是。

Is the environment noisy? With a good bit of variation both in target and background? Then you really, really do.

环境嘈杂吗？ 目标和背景都有很大的差异吗？ 那你真的，真的。

数据丰富，数据不良 (Rich data, Poor data)

Time waveform and high-resolution FFT are what we describe as “rich data.” There’s a lot of information in there, and they give analytical tools like ours which look for signatures and detect anomalies a great deal to work with. They make it possible to tell that, even though a machine is not vibrating “more” than it used to, it is vibrating “differently.”

时间波形和高分辨率FFT是我们所说的“丰富数据”。 那里有很多信息，它们提供了像我们这样的分析工具，这些工具可以寻找特征并检测异常，以供处理。 他们可以告诉我们，即使一台机器振动的程度没有以前那么大，但它的振动却有所不同。

RMS and Peak-to-Peak kinds of measures, on the other hand, are “poor data.” They don’t tell you much, and discard much of the information necessary to make the judgments that you most want to make. They’re basically just high-level descriptive statistics that discard almost all the essential signature information you need to find granular events and conditions that justify the value of the sensor implementation in the first place. And as this excellent example from another domain shows, descriptive statistics just don’t let you see the most interesting things.

另一方面，RMS和“峰-峰值”测量是“不良数据”。 他们不会告诉您太多信息，并且会丢弃做出您最想做出的判断所需的许多信息。 它们基本上只是高级描述性统计信息，几乎丢弃了您需要的所有基本特征信息，这些信息需要找到粒度事件和条件，这些事件和条件首先证明了传感器实现的价值。 而且，正如另一个领域的出色示例所示，描述性统计信息无法让您看到最有趣的内容。

In practical terms for vibration analysis, what does that mean?

实际上，对于振动分析，这意味着什么？

It means that by relying only on high-level descriptive statistics (poor data) rather than the time and frequency domains (rich data), you will miss anomalies, fail to detect signatures, and basically sacrifice most of the value that your implementation could potentially deliver. Yes, it may be more complicated to implement. It may be more expensive. But it can deliver exponentially higher value.

这意味着仅依靠高级描述性统计信息(不良数据)而不是时域和频域(丰富数据)，您将错失异常，无法检测签名，并基本上牺牲了实现可能潜在的大部分价值。交付。 是的，实施起来可能会更复杂。 它可能更昂贵。 但是它可以提供成倍的价值。

Previously published at: https://reality.ai/rich-data-poor-data-getting-the-most-out-of-sensors/

先前发布于： https : //reality.ai/rich-data-poor-data-getting-the-most-out-of-sensors/