根据McKinsey, global gas demand is expected to reach 4,503 billion cubic meters (bcm) in 2035, showing a more than 1% annual growth from 3,736 bcm in 2017. Asia is expected to be the biggest consumer of the resource at 47%, followed by the rest of the world (24%), the Middle East (16%), and the United States (14%).
AI and the internet of things (IoT) have found their way into the油和气world. As of now, numerous companies claim to assistplant managers, machine operators, and field personnel predict什么时候machines need maintenance and upkeep, keep employees safe in the work environment, and improve production。
IOT是允许设备，机器和其他设备彼此通信的技术。它使石油和天然气公司能够使用数据科学方法管理和存储数据，创建应用程序和设置安全协议。
在本文中，我们列出了四家软件解决方案公司，该公司声称提供石油和天然气公司的IOT解决方案，从而利用人工智能所谓的所谓预测维护: using AI to know when to repair machine parts before they break entirely.
有兴趣的读者可能想阅读我们的文章预测一种nalytics in oil and gas, as预测(and prescriptive) analytics makes IoT-enabled predictive maintenance possible for oil and gas companies. For the sake of understanding this article, prescriptive analytics is simply predictive analytics with the extra step of recommending an action to a user.
对于石油和天然气公司来说，规定的分析软件可能会告诉用户他们应该修复特定的机器，因为它可能会在未来一周内突破。
预测性维护：AI修复石油和燃气厂机械
为石油和天然气行业的支持IOT的人工智能解决方案几乎总是提供石油和天然气公司的能力，了解其机器，管道和坦克时的工作不当或随时不当。这使他们能够在休息之前修复机器，这可以节省数百万的维修费用，监管费和罚款，并恢复到故障期间受伤的任何工人。
在本节中，我们将讨论三家供应商，该公司在声称通过边缘计算提供预测性维护的文章之后讨论之前，请提出更传统的预测性维护解决方案。
我们通过Spark认知开始进行分析，我们相信该公司最有可能在其解决方案中利用人工智能：
闪耀
闪耀is aT.exas-based company that offers一种suite of applications它声称可以提供帮助油和气businessesorganize, extract, classify, analyze, and process data to conduct maintenance on machinery and other equipment。
闪耀explains它的申请套件包括：
- Deepnlp，其中提取和组织各种企业来源的数据以启用分析
- SparkPredict，使用机器学习算法分析传感器数据来识别即将发生的机械故障
- Darwin, which trains the algorithms and builds the algorithmic models
- DeepArmor，被培训，以识别恶意软件来保护和安全的客户系统
T.he modules work together to predict deviations in the optimal performances of machinery. For instance, unstructured data from a variety of sources are first collected by DeepNLP. The application classifies the data based on its attributes, allowing SparkPredict’s machine learning capabilities to filter and compare the new data with those in the database. If the algorithms detect sub-optimal performance in the machinery, the application alerts the plant operators before machine failure occurs.
我们可以推断出来油和气客户公司的专家需要确定在哪里安装传感器glycol system and export compressors。T.hese sensors would then collect telemetric data from those parts of theglycol system and export compressors， 如压力，振动和温度。
然后将此数据用作正确运行的基线glycol system and export compressors。
软件背后的机器学习模型需要在数百万这些遥测数据点和数据上进行培训，以及某些部分的数据glycol system and export compressors所需的维护，对这些部件进行维护多长时间，并且可能需要多长时间的零件到达现场。
然后将通过软件的机器学习算法运行数据。这将培训算法来辨别所有这些数据点中的哪一个与正常运行相关glycol system and export compressorsparts, the time at which theglycol system and export compressorshave needed maintenance in the past, and which of its parts needed repair.
T.he software would then be able to predict when certain parts of theirglycol system and export compressors在他们分解之前是由于维护。
达尔文模块的功能似乎使数据科学家能够构建算法模型。该公司声称，此模块清除数据然后标识最佳构建模型架构的属性。SparkCognition表示达尔文还展示了如何实现结果。数据科学家可以添加更多数据集以提高准确性，该公司报告。
下面是一个很短的3.-minute video demonstrating howSparkPredict作品：
闪耀声称有帮助一个未命名的oil exploration and production (E&P) company为预测维护创建算法模型，避免昂贵的应急维修，提高操作安全性，并在其所有17个油井中最大化生产。
根据案例研究,turne勘探生产公司d to SparkCognition to create models that could predict well maintenance and production. Using Darwin, SparkCognition’s data scientists used the oil well’s location and reservoir attributes such as monthly oil, water and gas production datasets to train the algorithmics. In all, the models referenced 40 attributes to recognize seven maintenance event types related to the oil wells.
案例研究报告说，该系统帮助工程师预测潜在的侵入性讨论，杆变化，以及12个井中的清洁操作，精度为70％至80％，降低运营成本。
该模型还可以预测维护要求，在需要之前，帮助改善运营和工人安全，减少昂贵的应急维修，并允许工程师专注于更高富有成效的井以最大化回报。
案例研究报告说，SparkCognition在几天内创建了算法模型，仅使用一个月的数据来实现当时的准确度。然而，读者应该采取这种情况，因为客户未命名，留下了一粒盐。
喷雾装置L.istsApergy, MHPS Americas, and the British Army如some它的过去的客户。
Bruce Porteris首席科学官在闪耀。He持有A.PhDinComputer Science从加州大学欧文。搬运工继续服用计算机科学教授在德克萨斯大学，奥斯汀于2009年至2017年担任该部主席。
SOFTWEB解决方案
SOFTWEB解决方案is aT.exas-based company that offersIOT连接，它声称可以提供帮助油和气businesses维护或改善设备性能和工人安全使用what appears to be prescriptive analytics.
SOFTWEB解决方案claimsIOT Connect系统的一部分是SoftWeb Intelligence和Analytics（SIA）平台。随着附着在泵，阀门和其他机器仪表上的传感器收集数据，该公司解释说，该数据被发送到云端，其中由SIA机器学习算法存储和分析。
T.he algorithms would have already been trained on data that indicates the normal functioning of the pumps, valves, or gauges; as such, they would be able to alert personnel when these machine parts were in some way malfunctioning, recommending them that they repair the parts.
下面是一个很短的3.-minute video demonstrating howIOT连接作品：
SOFTWEB解决方案声称有帮助industrial company英格索兰检测即将到来的机器故障，并避免在客户的生产基地停机。Ingersoll Rand需要保持销售给客户的压缩机的高效率，以最大限度地减少生产过程中的无计计划的停机时间。他们需要一种解决方案来远程监测和对压缩机进行预测分析。
案例研究报告称，传感器将运营数据发送到IOT Connect，在那里分析以更好地了解所功能的设备。压缩机持续报告了油温，油压，第1阶段的温度，电机电流，系统压力和其他度量的状态，通过安全的互联网连接。这些传感器警告技术人员对机器的状态。如果操作参数指示异常行为，则通过电子邮件提醒维护人员。
T.his enabled the Ingersoll Rand’s technicians to remotely determine the specific component that was underperforming and bring that component to the customer site.
SOFTWEB解决方案一种lso listsBosch, Firestone, ABB, Fujifilm, Pepsi, Qualcomm, Siemens, GE如some它的过去的客户。
我们无法找到与强大的AI背景团队中的任何C级高管的证据，但该公司报告收入$4.4M in revenue annually。r瓦斯isthe founder and CEO在SOFTWEB解决方案。He拥有一个多发性硬化症inComputer Science从伊利诺伊州理工学院。
T.elit
T.elitis anItalian提供的公司一种n IoT解it claims can help油和气公司improve productivity, safety, and conduct preventive maintenance使用自然语言处理（NLP）。
T.elitclaimsthe solution includes the设备透明应用，可分析钻井设备，管道，卡车，火车，船和配备传感器的油箱的数据。
该公司声称，DeviceWise从计算机和传感器中收集多种数据，在植物环境中监控和控制机械。然后它似乎类似地与本报告中列出的其他预测维护和遥测软件类似。
T.he company states that the resulting data analytics can be displayed on smartphones, desktop computers, and tablets, while alerts are received as emails, RSS feed, or a calendar event. This enables the business to make changes in the process to improve production, as well as conduct preventive maintenance on machines to ensure the safety of workers.
下面是一个很短的2-minute video demonstrating howTelit软件作品：
T.elit声称有帮助Intelligent Sensing Anywhere（ISA）deploy sensors in consumer LPG tanks and collect information about how the end-user homes use energy sources。
ISA专注于LPG散装罐监控。ISA集成Telit的CE910-Dual和GE910-Quad V3模块，可提供移动和固定应用的连接，如自动售货机，销售点，跟踪，智能计量和远程信息处理设备。
Working with ISA C-Log to remotely monitor equipment for fuel and gas tanks, the system now collects data such as level reading in gas and other fuel tanks and alarm transmission, enabling ISA to generate a larger stream of data. No other details were provided in this case study.
T.elit一种lso listsAcclaim Energy，Aquarius Spectrum，Automile AB，Axentia，Bigbelly Solar，Bluewind，Corintech，MC机械系统，触控组和电视如some它的过去的客户。公司提出$25.5 million in funding从Voila Credit, Fortissimo Capital, 360 Capital, and 83North.
T.hat all said, we were unable to find any C-level executives with AI experience on the company’s team, and so it is less likely that the company is actually leveraging machine learning than if they employed several data scientists with PhDs, which is our standard whenvetting companies on their likelihood of leveraging AI。
启用IOT的边缘计算，用于预测维护
Foghorn
Foghornis a提供的公司software called闪电边缘情报, which may help油和气businessesconduct maintenance that reduces machine failures and downtime, reduces costs, and potentially increases ROI and customer satisfaction使用机器学习。
大多数边缘解决方案使用传感器收集数据，并将此数据发送到云以进行离线分析。然而，许多工业环境和设备缺乏或具有差的连通性。
Sometimes, poor connectivity on-site makes it difficult send the large amount of sensor data to the cloud, causing delays. By the time data reaches and is processed with other data in the cloud, it may be too late to take any action.
FogHorn claims that its Lighting Edge Intelligence solution provides a machine learning-driven analytics engine with a footprint of less than 256MB that already analyzes the data at the edge before它传统上将被发送到中央云存储。
目前尚不清楚闪电优势是一种异常检测或规范分析软件，尽管当它检测到机器部件不正确时，它似乎在客户公司提醒人员，但标准是预测维护软件的标准。
We might infer that the software is based on prescriptive analytics because the algorithm would need to be trained on data that indicates a regularly functioning machine part before a client company deploys it.
一个异常检测软件需要在能够开始预测机器零件故障之前几周到几个月，并且更有可能需要访问客户公司的云subject-matter experts and data scientists必要时向算法提供反馈。
由于Foghorn似乎涉及他们公司在边缘工作的能力，他们的软件似乎在规范的分析算法上运行。
For instance, the company explains that the application can monitor the operational data from an electric submersible pump. If a potential failure is detected, the system could automatically stop the pump to prevent damage and notify operations to repair or replace the ESP based on current machine health and maintenance models.
另一个用例是管道优化，其中解决方案可以关闭阀门并向移动设备发送警报，以避免对管道的重大中断或损坏。
Foghorn在其网站上没有特征案例研究，但在新闻稿中索赔它有助于Daihen Corporation识别生产错误，提高协作和数据准确性，每年消除5000小时的手动数据输入。
Daihen needed a faster way to analyze data from dozens of sensors that measured material condition from the previous manual process. The company turned to Foghorn to deploy radio frequency identification (RFID) infrastructure to track manufacturing and team efficiency, and sensors to monitor the condition of its Osaka factory.
T.he press release claims that Edge ML automated the monitoring of Daihen’s electric transformers manufacturing process. Sensors collected data on the temperature, humidity and dust levels via RFID to generate analytics related to the assembly of each part of the transformers, and tracked how long each stage took to complete.
Using the collected data and the resulting analysis, Daihan was purportedly able to improved parts of the manufacturing process.
Foghorn补充说，在六个月内，跟踪系统的逐步部署覆盖了大伦工厂的70％，预计将在今年内追踪所有流程。
We were unable to find any mention of enterprise-level clients onFoghorn他们的网站也不在任何新闻稿中，但它提出了4750万美元在资金中并由英特尔首都支持，通用电气，沙特阿美能源风险企业。
Sastry Malladiisthe CTO在Foghorn。He拥有一个多发性硬化症inControl Systems从the Indian Institute of Technology, Kharagpur。先前，Malladiserved asChief Architect at Stubhub, an eBay company, and as Distinguished Architect at eBay.
Header Image Credit: Rimkus
