The industrial plants consist of several types of assets. Sensor based IoT is employed for asset diagnostics and prognostics. The rotating parts of machine assets are often subjected to mechanical wear and tear. If monitoring is not done of this wear and tear, it may lead to the breakdown in the machines and unexpected shutdown in the plant. Apart from mechanical faults, machines can also develop electrical faults as well. Therefore, condition monitoring of these machines is very important for early stage fault detection to avoid unscheduled repairs, minimize downtime and hence, guarantee reliability, up-time, and sustainability of machines. Several non-invasive machine condition monitoring techniques are used using sensors. Most are based on sensing Current, Vibration, Acoustic emission from the machines[2]. To get these signals, transducers are needed. Remote monitoring needs IoT pipelines in place.

工业工厂由几种类型的资产组成。 基于传感器的物联网用于资产诊断和预测。 机器资产的旋转零件经常受到机械磨损。 如果不对这种磨损进行监控，则可能导致机器故障以及工厂意外停机。 除了机械故障外，机器还可能产生电气故障。 因此，这些机器的状态监视对于早期故障检测非常重要，这样可以避免计划外的维修，最大程度地减少停机时间，从而保证机器的可靠性，正常运行时间和可持续性。 使用传感器使用了几种非侵入性机器状态监视技术。 大多数是基于检测机器的电流，振动和声发射[2]。 为了获得这些信号，需要换能器。 远程监控需要物联网管道。

The Condition Monitoring of Equipment’s — both electrical and non-electrical — is one of the major requirements for Industrial IoT Industry 4.0. While the sensor based data driven solution looks like a Machine Learning problem, it is not possible to address the component level diagnostics with ML. E.g, the sensory data can be noisy; and depending on the SNR, traditional ML approach would train noise instead of required signal itself. Another problem is time domain data signatures are unable to differentiate, isolate or understand the underline problem with the machine without Signal transforms like Fourier, Wavelets, Time-Frequency, Hilbert etc.

设备的状态监控-电气和非电气-是工业物联网工业4.0的主要要求之一。 尽管基于传感器的数据驱动解决方案看起来像是机器学习问题，但无法使用ML解决组件级诊断问题。 例如，感觉数据可能很嘈杂； 根据信噪比，传统的机器学习方法将训练噪声而不是所需的信号本身。 另一个问题是时域数据签名无法在没有信号变换(例如傅立叶，小波，时频，希尔伯特等)的情况下使用机器来区分，隔离或理解下划线问题。

In the above example, the data with fault in Pinion is shown in time-domain. The signal, when trained on ML, will have difficulty to infer anything as it cannot differentiate noise from anomalies. Thresholding approach is also not a good idea under dynamic loads and noise.

在上面的示例中，小齿轮中有故障的数据以时域显示。 信号经过ML训练后，将很难推断出任何东西，因为它无法区分噪声和异常。 在动态负载和噪声下，阈值方法也不是一个好主意。

By using Fourier Transformation on the signal, we can map many signatures that point to particular anomaly in the machine. Since the patterns have distinct characteristics, this approach is unsupervised and do not require large historical data to create inference.

通过对信号使用傅立叶变换，我们可以映射许多指向机器中特定异常的签名。 由于模式具有鲜明的特征，因此此方法不受监督 ，不需要大量的历史数据即可进行推断。

Let’s look into another example of Elevator System Vibration. Difference between normal operations and abnormal pattern signatures are clearly visible in spectral domain.

让我们看一下电梯系统振动的另一个例子。 正常操作和异常图案签名之间的差异在光谱域中清晰可见。

Time domain signature of this system does not differentiate anything between normal and abnormal patterns. The low frequency harmonics spikes are clearly visible in Spectral domain.

该系统的时域签名不能区分正常模式和异常模式。 低频谐波尖峰在“频谱”域中清晰可见。

One more example where the spectrogram of the Acoustics of door operation is shown. The signal contains various operations of the door, and it is possible to identify the certain operations and create degradation models using Signal Processing.

另一个示例显示了门操作的声学频谱图。 该信号包含门的各种操作，并且可以使用信号处理来识别某些操作并创建降级模型。

什么才算是数字信号处理 (What Qualifies as Digital Signal Processing)

After seeing above examples, lets first identify what is not DSP. Well, Digital Signal Processing is not about Fourier Transform (FFT) or FFT is not DSP as widely misconstrued in the Data Science community. There are literally hundreds of Algorithms that fall under the umbrella of Digital Signal Processing. There is another stream which is fusion of Statistics and Signal Processing — Statistical Signal Processing. It applies ML and Signal Processing to derive inferences.

看完以上示例后，让我们首先确定不是DSP。 嗯， 数字 信号处理 与傅立叶变换(FFT)无关，或者FFT与DSP一样，在数据科学界中没有被广泛误解。 实际上，有数百种算法属于数字信号处理的范畴。 还有另一个流是统计和信号处理的融合-统计信号处理。 它应用ML和信号处理来推论。

Table below shows some selected algorithms

下表显示了一些选定的算法

In the example below, a Digital Filter is applied to specific cut-off frequency defined by ISO specification to the noisy signature that filters out only specific frequency components. It would otherwise be impossible to achieve using Moving Average. The Data is 3 Axial acceleration profile of an elevator @ 100Hz.

在下面的示例中，将数字滤波器应用于由ISO规范定义的特定截止频率，以仅过滤特定频率分量的嘈杂信号。 否则将无法使用移动平均线实现。 数据是电梯在100Hz时的3轴加速度曲线。

Digital signal processing and analog signal processing are streams within engineering that deals with sensor signals. DSP applications include audio and speech processing, sonar, radar and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, data compression, video coding, audio coding, image compression, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others.

数字信号处理和模拟信号处理是工程中处理传感器信号的流。 DSP应用包括音频和语音处理，声纳，雷达和其他传感器阵列处理，频谱密度估计，统计信号处理，数字图像处理，数据压缩，视频编码，音频编码，图像压缩，电信，控制系统，生物医学的信号处理工程和地震学等等。

DSP can involve linear or nonlinear operations. Nonlinear signal processing is closely related to nonlinear system identification and can be implemented in the time, frequency, and spatio-temporal domains. [1]

DSP可能涉及线性或非线性运算。 非线性信号处理与非线性系统识别密切相关，可以在时域，频域和时空域中实现。 [1]

如何将DSP用于AI / ML (How to use DSP for AI/ML)

There are two ways of applying DSP.

有两种应用DSP的方法。

DSP based Data Transformation–

基于DSP的数据转换–

In this methodology, DSP is simply used as an ETL tool. The transformed data is used as another feature of the dataset. The downstream blocks of ML/AI consume the data in statistical sense, and learns the underline patterns to derive inferences. Most Data Scientist use this approach but there are many use cases where this approach is sub-optimal as domain inference and understanding of transforms are missing.

在这种方法中，DSP仅用作ETL工具。 转换后的数据用作数据集的另一个功能。 ML / AI的下游块以统计意义使用数据，并学习下划线模式以得出推论。 大多数数据科学家都使用这种方法，但是在很多用例中，由于缺少域推断和对变换的理解，因此这种方法不是最佳的。

DSP based Design and Inferences -

基于DSP的设计和推论-

In this method, the data is prepossessed/transformed into other domain using DSP, but the domain knowledge of DSP is used to derive the inferences that are further used to enhance the downstream AI/ML algorithms. Here, deep understanding of Signals and Systems is prerequisite to create the best ML models.

在这种方法中，使用DSP将数据预设/转换为其他域，但是使用DSP的域知识来推导出推论，这些推论将进一步用于增强下游AI / ML算法。 在这里，对信号和系统的深入了解是创建最佳ML模型的前提。

The use cases, where diagnostic of inner components of assets are needed — not just trend — then this method is mandatory. As shown in Figure 1 , the gear-pinion issue cannot be detected by raw ML or DSP as ETL + ML. We need to understand what harmonics means, what is the meaning of energy in a frequency band, what is the level of noise, attenuation, phase relationships, ability to differentiate signal from noise etc. This applies to almost all asset classes — Motors, Pumps, Compressors, Generators, Conveyors, Engines etc.

在用例中，需要诊断资产的内部组成部分-不只是趋势-那么此方法是强制性的。 如图1所示，原始ML或DSP无法将齿轮小齿轮问题检测为ETL + ML。 我们需要了解谐波的含义，频带中的能量的含义，噪声的水平，衰减，相位关系，区分信号与噪声的能力等。这几乎适用于所有资产类别-电动机，泵，压缩机，发电机，输送机，发动机等

物联网传感器应用的DSP和AI工作流程 (DSP and AI Workflow of IoT Sensor Applications)

The Workflow of DSP based AI applications is shown above.

上面显示了基于DSP的AI应用程序的工作流程。

Data Acquisition Process —

数据采集​​过程 —

This process is the most critical for any applications. Decision to select Sensor Types, Sensor Specification, Sampling Frequency, Analog to Digital Conversions, Sensor interface play vital role in the success of the end goal of Diagnostics and Predictive Maintenance.

对于任何应用程序，此过程都是最关键的。 选择传感器类型，传感器规格，采样频率，模数转换，传感器接口的决定对于诊断和预测性维护最终目标的成功至关重要。

I have highlighted some key components of Sensor Interface in the below diagram. During the process, I had analysed some of the vendors, so their names are visible. However, there are many players in the market with different specifications, and selection of particular component depends on many domain related specification of the Machine/Source of interest and the end goal.

我在下图中突出显示了传感器接口的一些关键组件。 在此过程中，我分析了一些供应商，因此可以看到它们的名称。 但是，市场上有许多参与者具有不同的规格，并且特定组件的选择取决于感兴趣的机器/源和最终目标的许多领域相关规格。

Many off the shelf smart sensor providers have integrated ADC, Interface and connectivity.

许多现成的智能传感器提供商都集成了ADC，接口和连接性。

Next is the Critical block of DSP — As explained earlier, this block performs the Signal Processing operations ranging from Filtering, transforms into multiple domains — Table 1

接下来是DSP的关键模块 -如前所述，该模块执行信号处理操作，范围从滤波，转换到多个域-表1

After DSP comes the Feature Engineering part where the features are extracted from the transformed data. These features are addition to original features from the dataset that provide great insights into the data that would otherwise not be possible.

DSP之后出现功能工程部分 ，从转换的数据中提取特征。 这些功能是数据集中原始功能的补充，这些功能可提供对数据的深刻见解，否则将无法实现。

Next we split into two branches. In the domain specific path, we infer the signatures that we got through multiple transformation from domain perspective and derive a diagnostic model from it. Further down the advance model training with diagnostic signatures can be used to train the AI model to derive RUL and other predictive maintenance related metrics.

接下来，我们分为两个分支。 在特定于域的路径中，我们从域角度推断通过多次转换获得的签名，并从中得出诊断模型。 再进一步，具有诊断签名的高级模型训练可用于训练AI模型，以得出RUL和其他与预测性维护相关的指标。

The other path is statistical inference based approach that is used to give additional information to model building but is purely statistical in nature.

另一条路径是基于统计推断的方法，该方法用于为模型构建提供其他信息，但本质上是纯统计方法。

结论 (Conclusions)

We learned what is Digital Signal Processing and what is its significance for IoT sensor based use cases.

我们了解了什么是数字信号处理以及其对于基于IoT传感器的用例的意义。

We saw the Difference between traditional Statistical Based AI modeling and Signal Processing based approach.

我们看到了传​​统的基于统计的AI建模与基于信号处理的方法之间的区别。

We saw the Sensor Interface Specification with reference to upstream and downstream integration, ADC, Sampling Rate, Connectivity.

我们看到了有关上游和下游集成，ADC，采样率，连接性的传感器接口规范。

We discussed DSP based AI workflow.

我们讨论了基于DSP的AI工作流程。